System and method for image segmentation

ABSTRACT

Methods and systems for image processing are provided. Image data may be obtained. The image data may include a plurality of voxels corresponding to a first plurality of ribs of an object. A first plurality of seed points may be identified for the first plurality of ribs. The first plurality of identified seed points may be labelled to obtain labelled seed points. A connected domain of a target rib of the first plurality of ribs may be determined based on at least one rib segmentation algorithm. A labelled target rib may be obtained by labelling, based on a hit-or-miss operation, the connected domain of the target rib, wherein the hit-or-miss operation may be performed using the labelled seed points to hit the connected domain of the target rib.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.17/035,612, filed on Sep. 28, 2020, which is a continuation of U.S.application Ser. No. 15/859,516, filed on Dec. 30, 2017, now U.S. Pat.No. 10,789,709, which is a continuation of U.S. application Ser. No.15/721,779, filed on Sep. 30, 2017, now U.S. Pat. No. 10,621,724, whichis a continuation of PCT Application No. PCT/CN2017/100024, filed onAug. 31, 2017, and also claims priority to Chinese Application Nos.201710939388.5 and 201710944072.5, filed on Sep. 30, 2017, and the U.S.application Ser. No. 15/859,516, filed on Dec. 30, 2017, now U.S. Pat.No. 10,789,709, is a continuation of PCT Application No.PCT/CN2017/100024, filed on Aug. 31, 2017, the contents of each of whichare hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to image processing, and moreparticularly, a system and method for rib recognition and segmentation.

BACKGROUND

Medical imaging has been widely used today. Segmentation and/orlabelling of ribs in a medical image may serve as a foundation foranalyzing the anatomical structure of ribs and/or diagnosis of variouskinds of diseases. Manual segmentation and/or labelling of ribs may betime consuming. Automatic segmentation and/or labelling may be achallenging task due to one or more factors including, for example, anunclear rib boundary, a rib may be adhesive to another bone, and amissing rib or a missing part of a rib because of, e.g., a pathologicalcondition, or the like, or a combination thereof. Therefore, it may bedesirable to develop an effective method and system for rib segmentationand/or labelling that may solve the problems mentioned above.

SUMMARY

One aspect of the present disclosure relates to a first method forprocessing an image. The method may be implemented on at least onemachine each of which has at least one processor and one storage. Themethod may include one or more of the following operations. Image datamay be acquired, wherein the image data may include a plurality of ribs.A rib region containing at least a portion of the plurality of ribs maybe determined. At least one rib of the plurality of ribs may be selectedas a target rib based on the rib region. At least onerib-probability-map relating to the target rib may be generated based onan artificial intelligence algorithm. A starting point of the target ribmay be determined based on the image data, wherein the starting pointmay indicate a starting position for tracking the target rib. At leastone portion of the target rib may be tracked based on the starting pointand the at least one rib-probability-map. A segmented rib may beobtained by segmenting the at least one portion of the target rib.

Another aspect of the present disclosure relates to a firstnon-transitory computer readable medium storing instructions. Theinstructions, when executed by at least one processor, may cause the atleast one processor to implement the first method.

A further aspect of the present disclosure relates to a first system forprocessing an image. The first system may include at least one processorand a storage configured to store instructions. The instructions, whenexecuted by the at least one processor, may cause the first system toeffectuate the first method.

A further aspect of the present disclosure relates to a second systemfor processing an image. The second system may include at least oneprocessor and a storage configured to store instructions. The secondsystem may include an image acquisition module configured to acquireimage data, the image data including a plurality of ribs; a ribpre-segmentation sub-module configured to determine a rib regioncontaining at least a portion of the plurality of ribs, and select atleast one rib of the plurality of ribs as a target rib based on the ribregion; a classification-probability-map determination block configuredto generate at least one rib-probability-map relating to the target ribbased on an artificial intelligence algorithm; a starting pointdetermination unit configured to determine a starting point of thetarget rib based on the image data, the starting point indicating astarting position for tracking the target rib; a rib model tracking unitconfigured to track at least one portion of the target rib based on thestarting point and the at least one rib-probability-map; and a ribboundary extraction unit configured to obtain a segmented rib bysegmenting the at least one portion of the target rib.

A further aspect of the present disclosure relates to a second methodfor processing an image. The method may be implemented on at least onemachine each of which has at least one processor and one storage. Themethod may include one or more of the following operations. Image datamay be acquired, wherein the image data may include a plurality ofvoxels corresponding to a first plurality of ribs of an object, areference voxel relating to a reference organ of the object. A firstplurality of seed points for the first plurality of ribs may beidentified. The first plurality of identified seed points may belabelled to obtain labelled seed points. A connected domain of a targetrib of the first plurality of ribs may be determined based on at leastone rib segmentation algorithm. The connected domain of the target ribmay be labelled based on a hit-or-miss operation, wherein the connecteddomain may include at least one of the labelled seed points, and whereinthe hit-or-miss operation may be performed using the labelled seedpoints to hit the connected domain of the target rib.

A further aspect of the present disclosure relates to a secondnon-transitory computer readable medium storing instructions. Theinstructions, when executed by at least one processor, may cause the atleast one processor to implement the second method.

A further aspect of the present disclosure relates to a third system forprocessing an image. The third system may include at least one processorand a storage configured to store instructions. The instructions, whenexecuted by the at least one processor, may cause the third system toeffectuate the second method.

A further aspect of the present disclosure relates to a fourth systemfor processing an image. The fourth system may include at least oneprocessor and a storage configured to store instructions. The fourthsystem may include an image acquisition module configured to acquireimage data, the image data including a plurality of voxels correspondingto a first plurality of ribs of an object, a reference voxel relating toa reference organ of the object; a seed point determination sub-moduleconfigured to identify a first plurality of seed points for the firstplurality of ribs; a rib pre-segmentation sub-module configured todetermine a connected domain of a target rib of the first plurality ofribs based on at least one rib segmentation algorithm; and a riblabelling sub-module configured to label the first plurality ofidentified seed points to obtain labelled seed points, and label theconnected domain of the target rib based on a hit-or-miss operation,wherein the connected domain includes at least one of the labelled seedpoints, and wherein the hit-or-miss operation is performed using thelabelled seed points to hit the connected domain of the target rib.

A further aspect of the present disclosure relates to a third method forprocessing an image. The method may be implemented on at least onemachine each of which has at least one processor and one storage. Themethod may include one or more of the following operations. A medicalimage may be acquired, wherein the medical image may include a pluralityof voxels corresponding to a plurality of ribs. A plurality of seedpoints of a plurality of first connected domains of the plurality ofribs may be identified based on a recognition algorithm. The medicalimage may be segmented to obtain a plurality of second connected domainsof the plurality of ribs. The plurality of ribs may be labelled bymatching the first connected domains including the plurality of seedpoints with the second domains of the plurality of ribs.

A further aspect of the present disclosure relates to a thirdnon-transitory computer readable medium storing instructions. Theinstructions, when executed by at least one processor, may cause the atleast one processor to implement the third method.

A further aspect of the present disclosure relates to a fifth system forprocessing an image. The fifth system may include at least one processorand a storage configured to store instructions. The instructions, whenexecuted by the at least one processor, may cause the fifth system toeffectuate the third method.

A further aspect of the present disclosure relates to a sixth system forprocessing an image. The sixth system may include at least one processorand a storage configured to store instructions. The sixth system mayinclude an image acquisition module configured to acquire a medicalimage, the medical image including a plurality of voxels correspondingto a plurality of ribs; a seed point determination sub-module configuredto identify a plurality of seed points of a plurality of first connecteddomains of the plurality of ribs based on a recognition algorithm; a ribpre-segmentation sub-module configured to segment the medical image toobtain a plurality of second connected domains of the plurality of ribs;and a rib labelling sub-module configured to label the plurality of ribsby matching the first connected domains including the plurality of seedpoints with the second domains of the plurality of ribs.

In some embodiments, the selection of at least one rib of the pluralityof ribs as a target rib based on the rib region may include one or moreof the following operations. A seed point for the at least one rib ofthe plurality of ribs may be determined. Pre-segmentation may beperformed based on the image data and the seed point to obtain apreliminary rib. The preliminary rib may be designated as the target ribfor further segmentation based on a determination that the preliminaryrib is adhesive to a vertebra. The preliminary rib may be designated asthe segmented rib based on a determination that the preliminary rib isnot adhesive to a vertebra.

In some embodiments, the determination of a starting point of the targetrib may include one or more of the following operations. A histogram maybe determined based on a plurality of image layers of the target rib ina coronal plane. A characteristic point of the target rib may bedesignated as the starting point based on the histogram.

In some embodiments, the determination of a histogram may include one ormore of the following operations. A plurality of rib pixels or voxels ofthe plurality of image layers may be superimposed along ananterior-posterior direction to obtain a diagram, wherein each elementat a position of the diagram may represent a total number of pixels orvoxels that are located at a corresponding position in one or more ofthe plurality of image layers and belong to a portion of the pluralityof rib pixels or voxels, and wherein each pixel or voxel of the portionof the plurality of rib pixels or voxels may have a gray value largerthan a first threshold. A plurality of elements of the diagram may besuperimposed along a superior-inferior direction to obtain thehistogram, wherein each element of the histogram may represent a sum ofelements belonging to a portion of the plurality of elements, andwherein all of the portion of the plurality of elements may have a sameposition in a left-right direction.

In some embodiments, the characteristic point may be determined based ona position in the histogram, wherein a point at the position may have aminimum value in the histogram.

In some embodiments, the generation of at least one rib-probability-maprelating to the target rib may include one or more of the followingoperations. The at least one rib-probability-map may be generated basedon a classifier, wherein the classifier may be trained based on theartificial intelligence algorithm and a plurality of images relating toat least one sample rib.

In some embodiments, the tracking of at least one portion of the targetrib may include one or more of the following operations. A tracedirection range may be determined based on the image data. A predictedrib segment may be determined based on the trace direction range and theat least one rib-probability-map to obtain the at least one portion ofthe target rib.

In some embodiments, the determination of a predicted rib segment mayinclude one or more of the following operations. At least one portion ofthe at least one rib-probability-map may be determined within the tracedirection range. A trace direction may be determined based on the atleast one portion of the at least one rib-probability-map. The predictedrib segment may be predicted based on the trace direction.

In some embodiments, the tracking of at least one portion of the targetrib may further include one or more of the following operations. Thepredicted rib segment may be matched with at least one rib model.

In some embodiments, the first method may further include one or more ofthe following operations. The tracking of the at least one portion ofthe target rib may be terminated based on a determination that thepredicted rib segment does not match with the at least one rib model.

In some embodiments, the first method may further include one or more ofthe following operations. Based on a determination that the predictedrib segment does not match with the at least one rib model, modelreconstruction may be performed based on a plurality of matched ribsegments to obtain a reconstructed model; at least one portion of thetarget rib may be extracted based on the plurality of matched ribsegments.

In some embodiments, the first method may further include one or more ofthe following operations. Based on a determination that the predictedrib segment matches with the at least one rib model, the predicted ribsegment may be designated as a matched rib segment of the target rib; anext rib segment of the target rib may be tracked based on the matchedrib segment of the target rib and the at least one rib-probability-map.

In some embodiments, the target rib may have a first end and a secondend, wherein the first end of the target rib may be spaced from avertebra by a first distance, and the second end of the target rib maybe spaced from the vertebra by a second distance, and the first distancemay be larger than the second distance.

In some embodiments, the determination of a starting point of the targetrib may include one or more of the following operations. A point of thetarget rib closer to the second end than to the first end of the targetrib may be designated as the starting point.

In some embodiments, the tracking of at least one portion of the targetrib may include one or more of the following operations. The at leastone portion of the target rib may be tracked from the starting point tothe second end of the target rib.

In some embodiments, the obtaining of a segmented rib by segmenting theat least one portion of the target rib may include one or more of thefollowing operations. A first portion of the target rib may be segmentedusing a first segmentation algorithm, wherein the first portion mayinclude a region between the starting point and the first end of thetarget rib. The first portion of the target rib and the segmented ribmay be combined to obtain the target rib.

In some embodiments, the first segmentation algorithm may be a regiongrowing algorithm.

In some embodiments, the first method may further include one or more ofthe following operations. The segmented rib may be labelled.

In some embodiments, the identification of a first plurality of seedpoints for the first plurality of ribs may include one or more of thefollowing operations. A middle image layer of the image data in acoronal plane near the middle of a lung of the object may be obtained. Asecond plurality of seed points of a second plurality of ribs may beidentified in the middle image layer. A plurality of image layers in atransverse plane of the image data containing at least one residual ribnot included in the middle image layer may be determined. At least oneseed point of the at least one residual rib may be identified.

In some embodiments, the identification of a second plurality of seedpoints of a second plurality of ribs in the middle image layer mayinclude one or more of the following operations. A lung mask may beobtained in the middle image layer. The lung mask may be dilated. Thesecond plurality of seed points of the second plurality of ribs in themiddle image layer may be identified based on the dilated lung mask.

In some embodiments, the labelling of the first plurality of identifiedseed points may include one or more of the following operations. Thesecond plurality of identified seed points of the second plurality ofribs in the middle image layer and the at least one seed point of the atleast one residual rib may be labelled based on an anatomical structureof the first plurality of ribs and the reference organ to obtainlabelled seed points.

In some embodiments, the labelling of the first plurality of identifiedseed points may include one or more of the following operations. A firstseed point of a first rib may be labelled based on a position of thereference voxel. A relative position between the first seed point of thefirst rib and a second seed point of a second rib may be determined. Thesecond seed point of the second rib may be labelled based on therelative position between the first seed point and the second seedpoint.

In some embodiments, the reference voxel may relate to an apex of a lungof the object or a base of a liver of the object.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIGS. 1A and 1B are schematic diagrams illustrating an exemplary imagingsystem according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device on which theprocessing engine may be implemented according to some embodiments ofthe present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device on which the terminalmay be implemented according to some embodiments of the presentdisclosure;

FIG. 4A is a schematic diagram illustrating an exemplary processingengine according to some embodiments of the present disclosure;

FIG. 4B is a flowchart illustrating an exemplary process for generatinga rib image according to some embodiments of the present disclosure;

FIG. 5A is a schematic diagram illustrating an exemplary rib extractionmodule according to some embodiments of the present disclosure;

FIG. 5B is a flowchart illustrating an exemplary process for extractinga rib according to some embodiments of the present disclosure;

FIG. 6A is a schematic diagram illustrating an exemplary ribsegmentation sub-module according to some embodiments of the presentdisclosure;

FIG. 6B is a flowchart illustrating an exemplary process for segmentinga rib according to some embodiments of the present disclosure;

FIG. 7A is a schematic diagram illustrating an exemplary rib modeltracking unit according to some embodiments of the present disclosure;

FIG. 7B is a flowchart illustrating an exemplary process for rib modeltracking according to some embodiments of the present disclosure;

FIG. 7C illustrates an exemplary trace direction range of rib modeltracking according to some embodiments of the present disclosure;

FIG. 7D illustrates an exemplary original rib image according to someembodiments of the present disclosure;

FIG. 7E illustrates an exemplary classification probability mapaccording to some embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for extracting arib according to some embodiments of the present disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for labellingone or more ribs according to some embodiments of the presentdisclosure;

FIG. 10A illustrates an exemplary middle image layer in the coronalplane according to some embodiments of the present disclosure;

FIG. 10B illustrates an exemplary middle image layer with a dilated lungmask in the coronal plane according to some embodiments of the presentdisclosure;

FIG. 10C illustrates an exemplary middle image layer with ten pairs ofribs in the coronal plane according to some embodiments of the presentdisclosure;

FIG. 10D illustrates an exemplary image layer (e.g., below the line) inthe transverse plane with a pair of residual ribs according to someembodiments of the present disclosure;

FIG. 10E illustrates exemplary labelled ribs according to someembodiments of the present disclosure; and

FIG. 11A through 11D illustrate exemplary test images of ribsegmentation using artificial intelligence based model trackingaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of example in order to provide a thorough understanding ofthe relevant application. However, it should be apparent to thoseskilled in the art that the present application may be practiced withoutsuch details. In other instances, well known methods, procedures,systems, components, and/or circuitry have been described at arelatively high-level, without detail, in order to avoid unnecessarilyobscuring aspects of the present application. Various modifications tothe disclosed embodiments will be readily apparent to those skilled inthe art, and the general principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the present application. Thus, the present application is notlimited to the embodiments shown, but to be accorded the widest scopeconsistent with the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by anotherexpression if they may achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2 ) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, module or block is referred toas being “on,” “connected to,” “communicate with,” “coupled to” anotherunit, module, or block, it may be directly on, connected or coupled to,or communicate with the other unit, module, or block, or an interveningunit, engine, module, or block may be present, unless the contextclearly indicates otherwise. As used herein, the term “and/or” includesany and all combinations of one or more of the associated listed items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

In an image processing, an image segmentation (or “recognition,”“classification,” “extraction,” “determination,” “identification,” etc.)may be performed to provide an image for a target region by dividing orpartitioning an image of a larger region including the target region. Insome embodiments, the imaging system may include one or more modalitiesincluding Digital Subtraction Angiography (DSA), Magnetic ResonanceImaging (MRI), Magnetic Resonance Angiography (MRA), Computed tomography(CT), Computed Tomography Angiography (CTA), Ultrasound Scanning (US),Positron Emission Tomography (PET), Single-Photon Emission ComputerizedTomography (SPECT), CT-MR, CT-PET, CE-SPECT, DSA-MR, PET-MR, PET-US,SPECT-US, TMS (transcranial magnetic stimulation)-MR, US-CT, US-MR,X-ray-CT, X-ray-MR, X-ray-portal, X-ray-US, Video-CT, Vide-US, or thelike, or any combination thereof. In some embodiments, the target regionmay be an organ, a texture, an object, a lesion, a tumor, or the like,or any combination thereof. Merely by way for example, the target regionmay include a head, a breast, a lung, a rib, a vertebra, a trachea, apleura, a mediastinum, an abdomen, a long intestine, a small intestine,a bladder, a gallbladder, a triple warmer, a pelvic cavity, a backbone,extremities, a skeleton, a blood vessel, or the like, or any combinationthereof. In some embodiments, the image may include a 2D image and/or a3D image. In the 2D image, its tiniest distinguishable element may betermed as a pixel. In the 3D image, its tiniest distinguishable elementmay be termed as a voxel (“a volumetric pixel” or “a volume pixel”). Insome embodiments, the 3D image may also be seen as a series of 2D slicesor 2D layers.

The segmentation process may be performed by recognizing one or morecharacteristic values or features of one or more pixels and/or voxels inan image. In some embodiments, the characteristic values or features mayinclude a gray level, a mean gray level, an intensity, texture, color,contrast, brightness, or the like, or any combination thereof. In someembodiments, one or more spatial properties of the pixel(s) and/orvoxel(s) may also be considered in a segmentation process.

For brevity, an image, or a portion thereof (e.g., a region of interest(ROI) in the image) corresponding to an object (e.g., a tissue, anorgan, a tumor, etc., of a subject (e.g., a patient, etc.)) may bereferred to as an image, or a portion of thereof (e.g., an ROI) of orincluding the object, or the object itself. For instance, an ROIcorresponding to the image of a rib may be described as that the ROIincludes a rib. As another example, an image of or including a rib maybe referred to a rib image, or simply a rib. For brevity, that a portionof an image corresponding to an object is processed (e.g., extracted,segmented, etc.) may be described as the object is processed. Forinstance, that a portion of an image corresponding to a rib is extractedfrom the rest of the image may be described as that the rib isextracted.

An aspect of the present disclosure relates to an image processingsystem and method for recognizing and/or segmenting ribs. To segment arib, the system and method may determine a starting point of the rib,segment the rib using artificial intelligence based model tracking basedon the starting point, and/or extract the rib. The system and method mayalso label the segmented rib based on one or more labelled seed pointsof the rib. The seed points of the rib may be determined based on arelative position of one or more pixels or voxels of the rib and a lungin a transverse plane of an image of the rib.

For illustration purposes, the following description is provided withreference to a segmentation process. It is understood that this is notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, a certain amount of variations,changes and/or modifications may be deducted under the guidance of thepresent disclosure. Those variations, changes and/or modifications donot depart from the scope of the present disclosure.

FIGS. 1A and 1B are schematic diagrams illustrating an exemplary imagingsystem 100 according to some embodiments of the present disclosure. Asshown, the imaging system 100 may include a scanner 110, a network 120,one or more terminals 130, a processing engine 140, and a storage device150. The components in the imaging system 100 may be connected in one ormore of variable ways. Merely by way of example, as illustrated in FIG.1A, the scanner 110 may be connected to the processing engine 140through the network 120. As another example, as illustrated in FIG. 1B,the scanner 110 may be connected to the processing engine 140 directly.As a further example, the storage device 150 may be connected to theprocessing engine 140 directly or through the network 120. As still afurther example, a terminal 130 may be connected to the processingengine 140 directly or through the network 120.

The scanner 110 may scan an object, and/or generate a plurality of datarelating to the object. In some embodiments, the scanner 110 may be amedical imaging device, for example, a PET device, a SPECT device, a CTdevice, an MRI device, or the like, or any combination thereof (e.g., aPET-CT device, a PET-MRI device, or a CT-MRI device). The scanner 110may include a gantry 111, a detector 112, a detection region 113, and atable 114. In some embodiments, the scanner 110 may also include aradioactive scanning source 115. The gantry 111 may support the detector112 and the radioactive scanning source 115. A subject may be placed onthe table 114 for scanning. In the present disclosure, “object” and“subject” are used interchangeably. The radioactive scanning source 115may emit radioactive rays to the subject. The detector 112 may detectradiation events (e.g., gamma photons) emitted from the detection region113. In some embodiments, the detector 112 may include one or moredetector units. The detector units may include a scintillationdetector(e.g., a cesium iodide detector), a gas detector, etc. The detector unitmay be and/or include a single-row detector and/or a multi-rowsdetector.

The network 120 may include any suitable network that can facilitateexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., thescanner 110, the terminal 130, the processing engine 140, the storagedevice 150, etc.) may communicate information and/or data with one ormore other components of the imaging system 100 via the network 120. Forexample, the processing engine 140 may obtain image data from thescanner 110 via the network 120. As another example, the processingengine 140 may obtain user instructions from the terminal 130 via thenetwork 120. The network 120 may be and/or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN), a wide area network (WAN)), etc.), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork, etc.), a cellular network (e.g., a Long Term Evolution (LTE)network), a frame relay network, a virtual private network (“VPN”), asatellite network, a telephone network, routers, hubs, switches, servercomputers, and/or any combination thereof. Merely by way of example, thenetwork 120 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 120 mayinclude one or more network access points. For example, the network 120may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the imaging system 100 may be connected to the network 120to exchange data and/or information.

The terminal(s) 130 may include a mobile device 131, a tablet computer132, a laptop computer 133, or the like, or any combination thereof. Insome embodiments, the mobile device 131 may include a smart home device,a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistance (PDA),a gaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 130 may be part of the processing engine 140.

The processing engine 140 may process data and/or information obtainedfrom the scanner 110, the terminal 130, and/or the storage device 150.For example, the processing engine 140 may process image data anddetermine a regularization item that may be used to modify the imagedata. In some embodiments, the processing engine 140 may be a singleserver or a server group. The server group may be centralized ordistributed. In some embodiments, the processing engine 140 may be localor remote. For example, the processing engine 140 may access informationand/or data stored in the scanner 110, the terminal 130, and/or thestorage device 150 via the network 120. As another example, theprocessing engine 140 may be directly connected to the scanner 110, theterminal 130 and/or the storage device 150 to access stored informationand/or data. In some embodiments, the processing engine 140 may beimplemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof. In some embodiments, theprocessing engine 140 may be implemented by a computing device 200having one or more components as illustrated in FIG. 2 .

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the terminal 130 and/or the processing engine 140. In someembodiments, the storage device 150 may store data and/or instructionsthat the processing engine 140 may execute or use to perform exemplarymethods described in the present disclosure. In some embodiments, thestorage device 150 may include a mass storage device, a removablestorage device, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage device 150 may be implemented on acloud platform. Merely by way of example, the cloud platform may includea private cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components in theimaging system 100 (e.g., the processing engine 140, the terminal 130,etc.). One or more components of the imaging system 100 may access thedata or instructions stored in the storage device 150 via the network120. In some embodiments, the storage device 150 may be directlyconnected to or communicate with one or more other components in theimaging system 100 (e.g., the processing engine 140, the terminal 130,etc.). In some embodiments, the storage device 150 may be part of theprocessing engine 140.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device 200 on which theprocessing engine 140 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2 , the computingdevice 200 may include a processor 210, a storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing engine 140 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataobtained from the scanner 110, the terminal 130, the storage device 150,and/or any other component of the imaging system 100. In someembodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both process A and process B, it should be understood thatprocess A and process B may also be performed by two or more differentprocessors jointly or separately in the computing device 200 (e.g., afirst processor executes process A and a second processor executesprocess B, or the first and second processors jointly execute processesA and B).

The storage 220 may store data/information obtained from the scanner110, the terminal 130, the storage device 150, and/or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage, a removable storage, a volatileread-and-write memory, a read-only memory (ROM), or the like, or anycombination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drives, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for the processing engine140 for determining a regularization item.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing engine 140. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touch screen, a microphone, or the like,or a combination thereof. Examples of the output device may include adisplay device, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Examples of the display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing engine 140 and thescanner 110, the terminal 130, and/or the storage device 150. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile networklink (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof. Insome embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 on which theterminal 130 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 3 , the mobile device 300 mayinclude a communication platform 310, a display 320, a graphicprocessing unit (GPU) 330, a central processing unit (CPU) 340, an I/O350, a memory 360, and a storage 390. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 300.In some embodiments, a mobile operating system 370 (e.g., iOS™,Android™, Windows Phone™, etc.) and one or more applications 380 may beloaded into the memory 360 from the storage 390 in order to be executedby the CPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating toimage processing or other information from the processing engine 140.User interactions with the information stream may be achieved via theI/O 350 and provided to the processing engine 140 and/or othercomponents of the imaging system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIG. 4A is a schematic diagram illustrating an exemplary processingengine 140 according to some embodiments of the present disclosure. Theprocessing engine 140 may include an image acquisition module 402, a ribextraction module 404, and a visualization module 406. At least aportion of the processing engine 140 may be implemented on a computingdevice as illustrated in FIG. 2 or a mobile device as illustrated inFIG. 3 .

The image acquisition module 402 may acquire image data. The imageacquisition module 402 may acquire the image data from the scanner 110or the storage device 150. In some embodiments, the image acquisitionmodule 402 may acquire the image data from an external data source viathe network 120. In some embodiments, the image data may correspond toX-rays that pass through a subject. In some embodiments, the radioactivescanning source 115 may emit the X-rays to the subject. The X-rays maypass through the subject and may attenuate during the passing-through.The extent of attenuation of an X-ray may depend on factors including,for example, the property of one or more tissues the X-ray passesthrough, the thickness of a tissue that the X-ray passes through, etc.The attenuated X-rays may be detected by the detector 112 andtransmitted to the image acquisition module 402. The image acquisitionmodule 402 may acquire image data at various times, via various devicesand/or under various conditions (e.g., weather, illuminance, scanningposition and angle, etc.). In some embodiments, the image acquisitionmodule 402 may acquire a reference voxel relating to a reference organof an object.

The image data may relate to one or more parts of a subject. In someembodiments, the image data may be generated by a full scanning of asubject using the scanner 110, and the image data may includeinformation regarding the whole subject. In some embodiments, the imagedata may be generated by a scanning of a portion of the subject usingthe scanner 110, and the image data may include information relating toa portion of the subject, for example, a chest, a trunk, an upper limb,a lower limb, a head, an organ, a tissue, etc. The image data of thesubject may be 2D image data or 3D image data. The 3D image data mayinclude a plurality of voxels. The 2D image data may include a pluralityof pixels. The image data of the subject may be MRI image data, CT imagedata, and/or PET image data, or the like, or any combination thereof.The image data may include original data generated from the scanner 110,data processed based on the original data, parameters for imageprocessing, or the like, or a combination thereof. In some embodiments,the image data may include data associated with one or more ribs, one ormore other bones (e.g., a vertebra, a sternum, a scapula, a clavicle,etc.), and/or one or more other organs/tissues (e.g., a lung, a bloodvessel, etc.). In some embodiments, a rib represented by one or morepixels/voxels may be adhesive to another bone (e.g., a vertebra)represented by one or more pixels/voxels in the image data.

The rib extraction module 404 may extract one or more ribs. The ribextraction module 404 may include one or more sub-modules, asillustrated in FIG. 5A. For convenience, a rib may refer to a set ofpixels/voxels representing a rib structure in the image data. A humanbody may have 12 pairs of ribs (i.e., 24 individual ribs). In someembodiments, a rib may be at one end connected to a vertebra. In someembodiments, the rib may be also connected to a sternum at the otherend. The two lungs and the liver of a subject may be located within therib cage formed by the 12 pairs of ribs. Among the 12 pairs of ribs, thepair of ribs closest to the head of a subject may be named as the firstpair of ribs, while the pair of ribs farthest away from the head may benamed as the twelfth pair of ribs. Other 10 pairs of ribs may besuccessively named as the second pair of ribs, the third pair of ribs,until the eleventh pair of ribs, in an order from the first pair to thetwelfth pair. For a pair of ribs, a rib close to the right hand may bereferred to as a right rib, while a rib close to the left hand may bereferred to as a left rib. For example, for the first pair of ribs, therib close to the right hand may be named as the first right rib, whilethe rib close to the left hand may be named as the first left rib. Otherindividual ribs may be named likewise. The first left rib may be closeto the apex of the left lung, while the first right rib may be close tothe apex of the right lung. The eleventh and/or the twelfth right ribmay be close to a lower border of the liver.

The rib extraction module 404 may extract rib(s) based on the image dataacquired by the image acquisition module 402. The extracted rib(s) mayinclude the set of pixels/voxels representing the rib(s) structure inthe image data. In some embodiments, the extracted rib(s) may includepixels/voxels at a boundary of the rib(s) and/or pixels/voxels withinthe boundary. In some embodiments, the extracted rib(s) may include oneor more pixels/voxels not belonging to the rib(s). For example, theextracted rib(s) may include one or more pixels/voxels representinganother bone (e.g., a vertebra, a sternum, a scapula, a clavicle, etc.),another tissue (e.g., a lung, a blood vessel, etc.), etc. The ribextraction module 404 may extract the rib(s) based on one or moresegmentation algorithms mentioned in the present disclosure.

The visualization module 406 may visualize the extracted rib(s) and/orimage data. The visualization module 406 may convert the image dataand/or the extracted rib(s) into a visual format including, for example,an image. The image may be a grayscale image or a color image. The imagemay be a 2D image or a 3D image. The image may be shown via a displaydevice (e.g., the I/O 230, the display 320, etc.) or printed by aprinter. The image may be presented to a user. In some embodiments, theimage may be stored in a storage device (e.g., the storage device 150,the storage 220, the storage 390, etc.) for further analysis.

FIG. 4B is a flowchart illustrating an exemplary process 400 forgenerating a rib image according to some embodiments of the presentdisclosure. In some embodiments, the process 400 may be performed by theprocessing engine 140. The process 400 may include acquiring image data401, extracting one or more ribs based on the image data 403, andvisualizing the extracted ribs 405. At least a portion of the process400 may be implemented on a computing device as illustrated in FIG. 2 ora mobile device as illustrated in FIG. 3 .

In 401, image data of a subject may be acquired. The subject may be ahuman body, an animal, or any part thereof. For example, the subject maybe an entire human body, an upper part of a human body, or the chest ofa human body, etc. In some embodiments, the image data may be acquiredby the image acquisition module 402. The image data may be acquired fromthe scanner 110, the storage device 150, and/or the terminal 130. Insome embodiments, the image data may be acquired from the I/O 230 of thecomputing device 200 via the communication port 240. In someembodiments, the image data may be acquired from an external data sourcevia the network 120. In some embodiments, the image data may bepre-processed to make the image data suitable for segmentation. Thepre-processing may include image normalization, image reconstruction,image smoothing, suppressing, weakening and/or removing a detail, amutation (e.g., a gray level mutation, etc.), noise, or the like, or anycombination thereof.

In 403, one or more ribs may be extracted based on the image dataacquired in 401. More descriptions of the rib extraction may be foundelsewhere in the present disclosure. See, for example, FIG. 5B and thedescription thereof. In some embodiments, 403 may be performed by therib extraction module 404. In 403, one or more pixels/voxels in theimage data that correspond to rib(s) may be identified and/or extracted.The rib(s) may be extracted based on one or more segmentationalgorithms. In some embodiments, the segmentation algorithms may includea threshold segmentation algorithm, a region growing segmentationalgorithm, an energy-based 3D reconstruction segmentation algorithm, alevel set-based segmentation algorithm, a region split and/or mergesegmentation algorithm, an edge tracking segmentation algorithm, astatistical pattern recognition algorithm, a C-means clusteringsegmentation algorithm, a deformable model segmentation algorithm, agraph search segmentation algorithm, a neural network segmentationalgorithm, a geodesic minimal path segmentation algorithm, a targettracking segmentation algorithm, an atlas-based segmentation algorithm,a rule-based segmentation algorithm, a coupled surface segmentationalgorithm, a model-based segmentation algorithm, a deformable organismsegmentation algorithm, a model matching algorithm, an artificialintelligence algorithm, or the like, or any combination thereof. In someembodiments, one or more segmentation algorithms may be stored in thestorage device 150, the storage 220, the storage 390, or another mobilestorage device (e.g., a mobile hard disk, a USB flash disk, or the like,or a combination thereof). In some embodiments, the segmentationalgorithms may be retrieved from one or more other external sources viathe network 120. In some embodiments, one or more seed points of one ormore ribs may be determined, and then one or more ribs may be extractedbased on one or more rib segmentation algorithms. In some embodiments,one or more seed points that are close to each other may be determined,and a connected domain relating to a rib may be determined by performinga dilating operation based on the seed point(s).

In 405, the rib(s) extracted in 403 may be visualized. In someembodiments, 405 may be performed by the visualization module 406. Theextracted rib(s) may be visualized based on one or more algorithms, suchas an image conversion algorithm, an image display algorithm, or thelike, or any combination thereof. The image conversion algorithm may beperformed to convert the extracted rib(s) from a frequency domain intoan image domain, from grayscale to color, etc. The image displayalgorithm may be performed to adjust color, contrast, brightness, etc.of the rib(s). In some embodiments, the rib(s) may be visualizedtogether with a background (e.g., a chest, a vertebra, a sternum, ascapula, a clavicle, etc.).

In some embodiments, in 405, the extracted rib(s) may be post-processed.The post-processing may be performed based on techniques including, forexample, a 2D post-processing technique, a 3D post-processing technique,or the like, or a combination thereof. Exemplary 2D post-processingtechniques may include a multi-planar reformation (MPR), a curved planarreformation (CPR), a computed volume reconstruction (CVR), a volumerendering (VR), or the like, or any combination thereof. Exemplary 3Dpost-processing technique may include a 3D surface reconstruction, a 3Dvolume reconstruction, a volume intensity projection (VIP), a maximumintensity projection (MIP), a minimum intensity projection (Min-IP), anaverage intensity projection (AIP), an X-ray simulation projection, avolume rendering (VR), or the like, or any combination thereof. Othertechniques may include a repair process, a rendering process, a fillingprocess, or the like, or any combination thereof. The repair process mayrestore a missing part (e.g., a rib fracture) of the extracted rib(s)based on information available in the existing part of the extractedrib(s). For example, the repair process may restore one or more missingpixels/voxels corresponding to an extracted rib based on one or moreavailable pixels/voxels close to the missing pixel(s)/voxel(s).

It should be noted that the above description about the processingengine 140 and the process 400 for generating a rib image is merelyprovided for the purpose of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations and/or modifications may be made under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, 403 and 405 may be combined into a single operation. Asanother example, after the extracted ribs in 405 is visualized, theprocess 400 may return to 403 for further extracting ribs in the imagedata. As still another example, 403 and 405 may be implementedsimultaneously. One or more other operations may be added to process400, or one or more operations may be omitted from process 400. Forexample, an operation for scanning the subject may be added before 401,which may be implemented by the scanner 110. As another example, anoperation for storing data may be added between or after 401, 403,and/or 405. The data may be stored in the storage device 150, thestorage 220, the storage 390, or an external database (not shown).

FIG. 5A is a schematic diagram illustrating an exemplary rib extractionmodule 404 according to some embodiments of the present disclosure. Therib extraction module 404 may include a seed point determinationsub-module 502, a rib pre-segmentation sub-module 504, a ribsegmentation sub-module 506, and a rib labelling sub-module 508. Atleast a portion of the rib extraction module 404 may be implemented on acomputing device as illustrated in FIG. 2 or a mobile device asillustrated in FIG. 3 .

The seed point determination sub-module 502 may determine one or moreseed points of a rib. In some embodiments, a seed point may include apixel/voxel defining an initial position for rib extraction. In someembodiments, a seed point may include a cluster of pixels/voxelsdefining an initial region for a rib extraction. A seed point may bepixel(s)/voxel(s) belonging to the rib. In some embodiments, a seedpoint may be one or more random pixels/voxels located in a region of therib. In some embodiments, a seed point may be one or more characteristicpixels/voxels located in a region of the rib. A characteristicpixel/voxel may refer to a pixel/voxel having a characteristic value(e.g., a gray level, brightness, etc.) or a characteristic position. Insome embodiments, the seed point determination sub-module 502 maydetermine the seed point(s) based on an anatomical structure of thesubject. Subjects of a same species may have similar anatomicalstructures, and thus seed point(s) may be determined based on theanatomical structure. In some embodiments, the seed point determinationsub-module 502 may determine the seed point(s) based on the position ofa rib relative to an organ (e.g., a lung, a liver, etc.) of the subject.For example, the seed point(s) of a first left rib and/or a first rightrib may be determined based on the position of an apex of a left lungand/or a right lung, and then the seed points of other ribs may bedetermined based on the seed point(s) of the first left rib and/or thefirst right rib. In some embodiments, the seed point determinationsub-module 502 may determine the seed point(s) based on a thresholdrelating to an image-related value (e.g., a gray level). For example,pixels/voxels having a pixel value (e.g., a CT value, a gray level,etc.) within a threshold range may be designated as the seed point(s).In some embodiments, the seed point determination sub-module 502 maylabel one or more seed points. In some embodiments, the seed pointdetermination sub-module 502 may label the seed point(s) correspondingto the first pair of ribs based on the position of the apex of a lung,and then the seed points corresponding to other ribs may be successivelylabelled from up to bottom. In some embodiments, the seed pointdetermination sub-module 502 may label the seed point(s) correspondingto the twelfth pair of ribs based on the base of a liver, and then theseed points corresponding to other ribs may be successively labelledfrom bottom to top. In some embodiments, an image layer in the coronalplane may include twelve pairs of ribs, and the seed pointscorresponding to the twelve pairs of ribs may be labelled based on theposition of the apex of a lung or the base of a liver. In someembodiments, the image data may include only a portion of a thorax(e.g., the image data may include a first pair of ribs but not a twelfthpair of ribs), the seed points may be successively labelled based on theapex of a lung. In some embodiments, the image data may include only aportion of a thorax (e.g., the image data may include a twelfth pair ofribs but not a first pair of ribs), the seed points may be successivelylabelled based on the base of a liver.

The rib pre-segmentation sub-module 504 may pre-segment one or more ribsbased on the image data. In some embodiments, the rib pre-segmentationsub-module 504 may determine a portion of a plurality of pixels/voxelsbelonging to the rib(s) in the image data. In some embodiments, the ribpre-segmentation sub-module 504 may determine one or more rib regions.In some embodiments, the rib pre-segmentation sub-module 504 may selectat least one rib of a plurality of ribs as a target rib based on the ribregion(s). For example, the rib pre-segmentation sub-module 504 mayperform pre-segmentation based on the image data and one or more seedpoints to obtain a preliminary rib. The rib pre-segmentation sub-module504 may designate the preliminary rib as a target rib for furthersegmentation based on a determination that the preliminary rib isadhesive to a vertebra. The rib pre-segmentation sub-module 504 maydesignate the preliminary rib as a segmented rib based on adetermination that the preliminary rib is not adhesive to a vertebra.The rib pre-segmentation sub-module 504 may determine a connected domainfor a pre-segmented rib. In some embodiments, the rib(s) that can berelatively easily segmented may be extracted in pre-segmentation. Therib pre-segmentation sub-module 504 may pre-segment rib(s) based on oneor more segmentation algorithms mentioned in the present disclosure. Insome embodiments, the rib pre-segmentation sub-module 504 may employ arelatively simple algorithm, which may save time and accelerate theprocess of rib extraction. For example, the rib pre-segmentationsub-module 504 may pre-segment the rib(s) using an edge detectionalgorithm based on a Laplace operator. It should be noted that the ribpre-segmentation sub-module 504 may be unnecessary.

In some embodiments, the rib pre-segmentation sub-module 504 maydetermine whether the rib pre-segmentation is successful. In someembodiments, the determination may be performed based on positioninformation of a pre-segmented rib and another bone (e.g., a vertebra,or the like). In some embodiments, the rib pre-segmentation may bedetermined to be unsuccessful when the pre-segmented rib(s) containingone or more pixels/voxels representing another bone (e.g., a vertebra,or the like); otherwise, the rib-segmentation may be determined to besuccessful. In some embodiments, the rib pre-segmentation sub-module 504may successfully pre-segment one or more ribs that are not heavilyadhesive to another bone. For a rib that is heavily adhesive to a bone,the rib may be connected to the bone, and a connected domain may begenerated between the rib and the bone. It may be difficult to identifywhether the connected domain belongs to the rib or the bone, or it maybe difficult to distinguish the connected domain from the rib and/or thebone. For example, the connected domain may be generated between a jointof the rib and a joint of the bone. If the difference between a graylevel of a pixel/voxel in the connected domain and a gray level of therib or the bone is less than a threshold, it may indicate that it isdifficult to distinguish the connected domain from the rib and/or thebone. As another example, if the distance between a boundary of the riband a boundary of the bone is less than a threshold, the connecteddomain may be generated at the boundary of the rib and the boundary ofthe bone due to partial volume effect, and it may indicate that it isdifficult to identify whether the connected domain belongs to the rib orthe bone. For a rib that is not heavily adhesive to a bone, the rib maybe connected to the bone, and a connected domain may be generated, butit may be relatively easy to identify whether the connected domainbelongs to the rib or the bone, or it may be relatively easy todistinguish the connected domain from the rib and/or the bone. If thedifference between a grey level of a pixel/voxel in the connected domainand a grey level of the rib or the bone is no less than a threshold, itmay indicated that it is relatively easy to distinguish the connecteddomain from the rib and/or the bone. In some embodiments, no rib may beconsidered successfully pre-segmented. For example, a first set ofpixels/voxels of rib(s) may overlap with a second set of pixels/voxelsof another bone; meanwhile, the first set of pixels/voxels and thesecond set of pixels/voxels may have similar pixel values (e.g., CTvalues, gray levels, brightness, etc.). Thus, it may be difficult todistinguish the rib(s) from another bone, and the rib pre-segmentationmay be considered unsuccessful.

The rib segmentation sub-module 506 may segment one or more rib(s) inthe image data. The rib segmentation sub-module 506 may determine aconnected domain for a segmented rib. In some embodiments, the ribsegmentation sub-module 506 may segment rib(s) based on a ribpre-segmentation result. For example, the rib segmentation sub-module506 may segment rib(s) that are not successfully segmented by the ribpre-segmentation sub-module 504. Thus the rib segmentation may be afurther process based on the rib pre-segmentation result. In someembodiments, the rib segmentation sub-module 506 may segment the rib(s)independently of the rib pre-segmentation sub-module 504. Thus, the ribsegmentation and the rib pre-segmentation may be performedindependently. In some embodiments, the rib segmentation sub-module 506may compare the rib segmentation result and the rib pre-segmentationresult, and/or identify a rib based on the comparison. The ribsegmentation sub-module 506 may include one or more units as describedin FIG. 6A.

The rib segmentation sub-module 506 may employ one or more algorithms tosegment rib(s). In some embodiments, the rib segmentation sub-module 506may employ different algorithms to segment different portions of a ribin the image data. For example, the rib segmentation sub-module 506 maysegment a first portion of a rib using a first algorithm (e.g., regiongrowing, or the like), and segment a second portion of the rib using asecond algorithm (e.g., model tracking, model matching, artificialintelligence algorithm, or the like) that is different from the firstalgorithm. In some embodiments, the rib segmentation sub-module 506 mayemploy a model tracking algorithm and/or an artificial intelligencealgorithm (e.g., an artificial intelligence based model trackingalgorithm) to segment a portion of a rib that is heavily adhesive toanother bone. Using the model tracking algorithm, a segmentation leakmay be prevented. A segmentation leak may refer to a segmentation errorthat determines a large number of non-rib pixels/voxels close to a ribregion as rib pixels/voxels. A rib pixel/voxel may refer to apixel/voxel representing a rib. A non-rib pixel/voxel may refer to apixel/voxel not representing a rib. Using the artificial intelligencealgorithm, the contrast between the rib(s) and other bones/organs in theimage data may be enhanced, thus the accuracy and robustness of thesegmentation may be improved. In some embodiments, the rib segmentationsub-module 506 may employ a single algorithm to segment an entire rib.

The rib labelling sub-module 508 may label one or more rib(s) segmentedin the image data. Labelling may refer to identifying and/or naming therib(s) in the image data. In some embodiments, the rib labellingsub-module 508 may label a rib based on the seed point(s) determined bythe seed point determination sub-module 502, the rib pre-segmentationresult obtained by the rib pre-segmentation sub-module 504, and/or therib segmentation result obtained by the rib segmentation sub-module 506.For example, when a seed point belonging to a first left rib is within aspatial range of a rib to be labelled, the rib may be labelled as “firstleft rib.”

In some embodiments, the rib labelling sub-module 508 may label one ormore ribs that are manually segmented by a user (e.g., a doctor, or thelike). For example, a lesion may appear in a fifth left rib of apatient, and the rib is not segmented automatically by the ribpre-segmentation sub-module 504 neither the rib segmentation sub-module506. However, a seed point of the fifth left rib may be determined bythe seed point determination sub-module 502. In this case, the user maymanually segment the rib, and the rib labelling sub-module 508 may labelthe rib as “fifth left rib” if the seed point of the “fifth left rib” iswithin the connected domain of the manually segmented rib. In someembodiments, the rib labelling sub-module 508 may label one or moreidentified seed points to obtain labelled seed points. In someembodiments, the rib labelling sub-module 508 may label one or moreconnected domains (or regions) of a target rib based on a hit-or-missoperation, wherein the connected domain(s) (or region(s)) may include atleast one of the labelled seed point(s), and wherein the hit-or-missoperation may be performed using the labelled seed point(s) to hit theconnected domain(s) (or region(s)) of the target rib. In someembodiments, the rib labelling sub-module 508 may label a plurality ofribs by matching one or more first connected domains (or regions)including a plurality of seed points with one or more second domains (orregions) of the plurality of ribs.

It should be noted that the above description of the rib extractionmodule 404 is merely provided for the purpose of illustration, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made to the rib extraction module 404 under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. For example, the ribpre-segmentation sub-module 504 may be removed, and there may be no ribpre-segmentation before the rib segmentation. As another example, therib labelling sub-module 508 may be removed, and the rib extractionmodule 404 may only extract rib(s) without labelling the rib(s). Asanother example, the seed point determination sub-module 502 and the riblabelling sub-module 508 may be combined into a single sub-module.

FIG. 5B is a flowchart illustrating an exemplary process 500 forextracting a rib according to some embodiments of the presentdisclosure. In some embodiments, the process 500 may be performed by therib extraction module 404. At least a portion of the process 500 may beimplemented on a computing device as illustrated in FIG. 2 or a mobiledevice as illustrated in FIG. 3 .

In 501, one or more seed points of a rib may be determined based on theimage data. In some embodiments, operation 501 may be performed by theseed point determination sub-module 502. The seed point(s) may be usedas a starting point for a rib pre-segmentation and/or rib segmentation.In some embodiments, the seed point(s) may be determined based on ananatomical structure of the subject. In some embodiments, the seedpoint(s) may be determined based on the position of the rib relative toan organ (e.g., a lung, a liver, etc.) of the subject. For example, theseed point(s) of a first left rib and/or a first right rib may bedetermined based on the position of an apex of a lung, and then the seedpoints of other ribs may be determined based on the seed point(s) of thefirst left rib and/or the first right rib. Operation 501 may beperformed automatically, semi-automatically, or manually. For example,the seed point(s) may be automatically determined as described above. Asanother example, a user may determine the seed point(s) through the I/O230. As still another example, the user may modify, add, delete, oradjust the automatically determined seed point(s).

In 503, a rib pre-segmentation may be performed based on the seedpoint(s) determined in 501. In some embodiments, a connected domain maybe determined in 503. The connected domain may include one or morepixels/voxels corresponding to the rib. In some embodiments, operation503 may be performed by the rib pre-segmentation sub-module 504. The ribpre-segmentation may be performed based on one or more algorithms. Insome embodiments, the rib pre-segmentation may be performed using anedge detection algorithm based on a Laplace operator. In someembodiments, whether the rib pre-segmentation is successful or not maybe determined in 503. In some embodiments, the determination may beperformed based on the position information of a pre-segmented rib andanother bone (e.g., a vertebra, or the like). In some embodiments, therib pre-segmentation may be determined to be unsuccessful when thepre-segmented rib contains one or more pixels/voxels representinganother bone (e.g., a vertebra, or the like); otherwise, therib-segmentation may be determined to be successful. In someembodiments, if the rib is not heavily adhesive to another bone, the ribpre-segmentation may be considered successful. In some embodiments, therib pre-segmentation may be considered unsuccessful. For example, afirst set of pixels/voxels of the rib may overlap with a second set ofpixels/voxels of another bone; meanwhile, the first set of pixels/voxelsand the second set of pixels/voxels may have similar pixel values (e.g.,CT values, gray levels, brightness, etc.). Thus, it may be difficult todistinguish the rib from another bone, and the rib pre-segmentation maybe considered unsuccessful. Operation 503 may be performedautomatically, semi-automatically, or manually. For example, the rib maybe automatically pre-segmented as described above. As another example, auser may pre-segment the rib through the I/O 230. As still anotherexample, the user may modify, or adjust an automatically pre-segmentedrib.

In 505, the rib may be segmented based on the rib pre-segmentationresult. In some embodiments, operation 505 may be performed by the ribsegmentation sub-module 506. In some embodiments, if the ribpre-segmentation is considered unsuccessful, the rib may be segmented in505. The rib may be segmented based on one or more segmentationalgorithms mentioned in the present disclosure. In some embodiments,different segmentation algorithms may be employed to segment differentportions of the rib. For example, a first portion of the rib may besegmented using a first algorithm (e.g., region growing, or the like),while a second portion of the rib may be segmented using a secondalgorithm (e.g., model tracking, model matching, artificial intelligencealgorithm, or the like) that is different from the first algorithm. Insome embodiments, a model tracking algorithm and/or an artificialintelligence algorithm (e.g., an artificial intelligence based modeltracking algorithm) may be employed to segment a portion of the rib thatis heavily adhesive to another bone. Using the model tracking algorithm,a segmentation leak may be prevented. Using the artificial intelligencealgorithm, the contrast between the rib and other bones/organs in theimage data may be enhanced, and thus the accuracy and robustness of thesegmentation may be improved. In some embodiments, a single algorithm(e.g., an artificial intelligence based model tracking) may be employedto segment the entire rib. In some embodiments, if the ribpre-segmentation is considered successful, operation 505 may be skipped.Operation 505 may be performed automatically, semi-automatically, ormanually. For example, the rib may be automatically segmented asdescribed above. As another example, a user may segment the rib manuallythrough the I/O 230. As still another example, the user may modify, oradjust an automatically segmented rib.

In 507, the rib pre-segmented in 503, and/or the rib segmented in 505may be labelled. In some embodiments, operation 507 may be performed bythe rib labelling sub-module 508. In some embodiments, the rib may belabelled based on the seed point(s) determined in 501. The rib may belabelled based on a position of the rib and the seed point(s). Forexample, if a seed point belonging to a first left rib is within aspatial range of the rib to be labelled, the rib may be labelled as“first left rib.” Operation 507 may be performed automatically,semi-automatically, or manually. For example, the rib may beautomatically labelled as described above. As another example, a usermay label the rib manually through the I/O 230. As still anotherexample, the user may modify, or adjust an automatically labelled rib.

It should be noted that the above description of the process 500 ismerely provided for the purpose of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be made tothe process 500 for rib extraction under the teachings of the presentdisclosure. However, those variations and modifications do not departfrom the scope of the present disclosure. For example, operation 503 maybe omitted. As another example, operations 503, 505, and/or 507 may beperformed simultaneously. In some embodiments, each segmented rib may belabelled immediately after the rib is pre-segmented or segmented. Insome embodiments, a rib may be labelled after all the ribs arepre-segmented or segmented.

FIG. 6A is a schematic diagram illustrating an exemplary ribsegmentation sub-module 506 according to some embodiments of the presentdisclosure. The rib segmentation sub-module 506 may include a startingpoint determination unit 602, a partial rib segmentation unit 604, a ribmodel tracking unit 606, and a rib boundary extraction unit 608. Atleast a portion of the rib segmentation sub-module 206 may beimplemented on a computing device as illustrated in FIG. 2 or a mobiledevice as illustrated in FIG. 3 .

The starting point determination unit 602 may determine one or morestarting points for rib segmentation. A starting point may include oneor more pixels/voxels representing a rib. In some embodiments, astarting point may indicate a starting position for tracking a targetrib. A starting point may be the same as or different from a seed pointdetermined by the seed point determination sub-module 502. The seedpoint(s) may provide a basis for rib pre-segmentation, while thestarting point(s) may provide a basis for rib segmentation. In someembodiments, the starting point determination unit 602 may designate arib pixel/voxel as a starting point randomly. In some embodiments, thestarting point determination unit 602 may designate a rib pixel/voxel ofa rib to be segmented as a starting point. A rib pixel/voxel may referto a pixel/voxel representing the rib. In some embodiments, the startingpoint determination unit 602 may determine a starting point based on athreshold relating to an image-related value (e.g., a gray level). Thethreshold may be the same as or different from the threshold appliedwhen determining a seed point of a rib. In some embodiments, thestarting point determination unit 602 may determine a starting pointbased on an anatomical structure of the subject. For example, thestarting point determination unit 602 may determine a rib pixel/voxelclose to a vertebra as a starting point of the rib. The rib pixel/voxelclose to the vertebra may indicate that the rib pixel/voxel may bewithin a certain range (e.g., 10 millimeters, 20 millimeters, 30millimeters, etc.) of the vertebra. In some embodiments, the startingpoint determination unit 602 may determine a rib pixel/voxel at thelowest position in the anatomical structure of the rib (i.e., a ribpixel/voxel closest to the back of the subject) as a starting point ofthe rib. For example, rib pixels/voxels (e.g., rib pixels/voxels of oneor more image layers in a coronal plane) may be added up (orsuperimposed) along an anterior-posterior direction (see FIG. 7C) of thesubject (e.g., a direction from the front to the back of the subject) toobtain a diagram representing the total number of rib pixels/voxels ineach coronal plane. In some embodiments, each element at a position ofthe diagram may represent a total number of pixels/voxels. Thepixels/voxels may be located at a corresponding position in one or moreof the image layers and may belong to a portion of the ribpixels/voxels, wherein each pixel/voxel of the portion of the ribpixels/voxels may have a gray value larger than a threshold. Then therib pixels/voxels in the diagram may be added up (or superimposed) alonga superior-inferior direction of the subject (e.g., a direction from thehead to the feet of the subject) to obtain a histogram representing adistribution of rib pixels/voxels along a left-right direction of thesubject (e.g., a direction from the left hand to the right hand of thesubject). In some embodiments, each element of the histogram mayrepresent a sum of elements of the diagram that have a same position ina left-right direction in the diagram. A position in the histogram witha minimum value may be determined as a coordinate on an X axis of thelowest position, wherein the X axis may correspond to a sagittal axis ofthe subject. A sagittal Y-Z plane relating to the coordinate on the Xaxis may be determined, wherein the Y-Z plane may correspond to acoronal plane of the subject. The starting point may be determined basedon an intersection part of the Y-Z plane and a pre-segmentation result(e.g., a pre-segmented rib). As illustrated in FIG. 7C, a ribpixel/voxel closest to the back of the subject (e.g., the ribpixel/voxel indicated by the arrow S in a first segment 752 of a rib)may be designated as the starting point. In some embodiments, one ribmay have one or more starting points.

The partial rib segmentation unit 604 may determine a first portion of arib using a segmentation algorithm based on a starting point determinedby the starting point determination unit 602. In some embodiments, thepartial rib segmentation unit 604 may start rib segmentation from thestarting point. In an anatomical structure, a rib may have two ends, afirst end and a second end. In some embodiments, the first end may befar from the vertebra, while the second end may be connected or close toa vertebra. In some embodiments, the first end may be spaced from thevertebra by a first distance, while the second end may be spaced fromthe vertebra by a second distance, and the first distance is larger thanthe second distance. A cross plane passing through the starting pointmay divide the rib into two portions. In some embodiments, the crossplane may refer to a plane perpendicular to a tangential direction of anouter surface of the rib at the starting point. In some embodiments, thestarting point may be closer to the second end than to the first end ofthe rib. The first portion of the rib may refer to a rib segment betweenthe starting point to the first end, while the second portion of the ribmay refer to a rib segment between the starting point to the second end.The partial rib segmentation unit 604 may determine the first portion ofthe rib based on one or more segmentation algorithms mentioned in thepresent disclosure.

The rib model tracking unit 606 may determine the second portion of therib using model tracking based on the starting point. The rib modeltracking unit 606 may include one or more sub-units as described in FIG.7A. In some embodiments, the rib model tracking unit 606 may start ribmodel tracking from the starting point. In some embodiments, the ribmodel tracking unit 606 may determine the second portion of the ribusing an artificial intelligence based model tracking algorithm. Theartificial intelligence algorithm may train a plurality of imagesrelating to at least one sample rib and generate a classifier forrecognizing a rib.

The rib boundary extraction unit 608 may extract a boundary of the rib.In some embodiments, the rib boundary extraction unit 608 may extractthe rib boundary based on the first portion of the rib determined by thepartial rib segmentation unit 604 and/or the second portion of the ribdetermined by the rib model tracking unit 606. In some embodiments, therib boundary extraction unit 608 may generate a whole rib based on thefirst portion of the rib and the second portion of the rib, and thenextract the boundary based on the whole rib. In some embodiments, one ormore pixels/voxels of the first portion may overlap with one or morepixels/voxels of the second portion near the starting point. Thus, therib boundary extraction unit 608 may fuse the intertwinedpixel(s)/voxel(s) and generate the whole rib. The rib boundaryextraction unit 608 may extract the rib boundary using one or morealgorithms including, for example, a Roberts edge detection algorithm, aSobel edge detection algorithm, a Prewitt edge detection algorithm, aLaplacian edge detection algorithm, a Log edge detection algorithm, aCanny edge detection algorithm, an algorithm based on a facet model, orthe like, or any combination thereof. In some embodiments, the ribboundary extraction unit 608 may obtain a segmented rib by segmenting atleast one portion of a target rib.

It should be noted that the above description of the rib segmentationsub-module 506 is merely provided for the purpose of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and/ormodifications may be made to the rib segmentation sub-module 506 underthe teachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, the partial rib segmentation unit 604 may be removed. Asanother example, the entire rib may be segmented by the rib modeltracking unit 606.

FIG. 6B is a flowchart illustrating an exemplary process 600 forsegmenting a rib according to some embodiments of the presentdisclosure. In some embodiments, the process 600 may be performed by therib segmentation sub-module 506. At least a portion of the process 600may be implemented on a computing device as illustrated in FIG. 2 or amobile device as illustrated in FIG. 3 .

In 601, a starting point for a target rib may be determined. In someembodiments, operation 601 may be performed by the starting pointdetermination unit 602. In some embodiments, a point of the target ribcloser to the second end than to the first end of the target rib may bedesignated as the starting point. In some embodiments, the startingpoint may be determined randomly based on one or more rib pixels/voxels.For instance, among a plurality of rib pixels/voxels of a rib to besegmented, a rib pixel/voxel may be designated as a starting point. Asanother example, among a plurality of rib pixels/voxels of a rib to besegmented, two or more neighboring rib pixels/voxels may be designatedas a starting point. As used herein, a pair of pixels/voxels may bereferred to as neighboring pixels/voxels if there is no otherpixel/voxel located between the pair of pixels/voxels; one of the pairmay be referred to as a neighboring pixel/voxel of the other. As usedherein, a plurality of pixels/voxels may be referred to as neighboringpixels/voxels if each pixel/voxel of the plurality of pixels/voxels hasa neighboring pixel/voxel that is also one of the plurality ofpixels/voxels.

In some embodiments, the starting point may be determined based on athreshold. For example, a rib pixel/voxel having a CT value greater thanthe threshold may be determined as the starting point. In someembodiments, the starting point may be determined based on an anatomicalstructure of the subject. For example, a rib pixel/voxel close to avertebra may be determined as the starting point. That a rib pixel/voxelis considered close to the vertebra if the rib pixel/voxel is within acertain range (e.g., 10 millimeters, 20 millimeters, 30 millimeters,etc.) of the vertebra. In some embodiments, a rib pixel/voxel at thelowest position in the anatomical structure of the rib (i.e., a ribpixel/voxel closest to the back of the subject) may be designated as astarting point of the rib. For example, rib pixels/voxels (e.g., ribpixels/voxels of one or more image layers in a coronal plane) may beadded up (or superimposed) along an anterior-posterior direction (seeFIG. 7C) of the subject (e.g., a direction from the front to the back ofthe subject) to obtain a diagram representing the total number of ribpixels/voxels in each coronal plane. In some embodiments, each elementat a position of the diagram may represent a total number ofpixels/voxels. The pixels/voxels may be located at a correspondingposition in one or more of the image layers and may belong to a portionof the rib pixels/voxels, wherein each pixel/voxel of the portion of therib pixels/voxels may have a gray value larger than a threshold. Thenthe rib pixels/voxels in the diagram may be added up (or superimposed)along a superior-inferior direction of the subject (e.g., a directionfrom the head to the feet of the subject) to obtain a histogramrepresenting a distribution of rib pixels/voxels along a left-rightdirection of the subject (e.g., a direction from the left hand to theright hand of the subject). In some embodiments, each element of thehistogram may represent a sum of elements of the diagram that have asame position in a left-right direction in the diagram. A position inthe histogram with a minimum value may be determined as a coordinate onan X axis of the lowest position, wherein the X axis may correspond to asagittal axis of the subject. A sagittal Y-Z plane relating to thecoordinate on the X axis may be determined, wherein the Y-Z plane maycorrespond to a coronal plane of the subject. The starting point may bedetermined based on an intersection part of the Y-Z plane and apre-segmentation result (e.g., a pre-segmented rib). As illustrated inFIG. 7C, a rib pixel/voxel closest to the back of the subject (e.g., therib pixel/voxel indicated by the arrow S in a first segment 752 of arib) may be designated as the starting point. In some embodiments, onerib may have one or more starting points. Operation 601 may be performedautomatically, semi-automatically, or manually. For example, thestarting point may be automatically determined as described above. Asanother example, a user may determine the starting point manuallythrough the I/O 230. As still another example, the user may modify, add,delete, or adjust the automatically determined starting point.

In 603, a first portion of the target rib may be determined using asegmentation algorithm based on the starting point determined in 601. Insome embodiments, operation 603 may be performed by the partial ribsegmentation unit 604. Considering that the first portion of the targetrib is not adhesive to any vertebra, a convenient algorithm (e.g., athreshold segmentation, a region growing segmentation, etc.) may beinvolved in 603. The convenient algorithm may have a relatively highefficiency, and consume less computational capacity and/or time. In someembodiments, a region growing segmentation may be performed to segmentthe first portion of the target rib. Using region growing segmentation,pixel(s)/voxel(s) that are adjacent to the starting point and satisfyone or more conditions may be iteratively extracted as ribpixels/voxels. One or more limitations may be set for segmenting thefirst portion of the target rib when the region growing algorithm isinvolved. In some embodiments, the region growing may be performed alonga direction from the starting point to the first end of the target rib,and a newly grown region corresponding to the target rib may be limitednot to go beyond the cross plane passing through the starting point thatseparates the first portion and the second portion of the target rib. Insome embodiments, image data corresponding to the rib segmented usingregion growing may need to satisfy a condition. For example, such imagedata may satisfy a certain derivative (e.g., a second derivative)relationship, fall within a threshold range, or the like, or anycombination thereof.

In 605, a second portion of the target rib may be determined using modeltracking based on the starting point determined in 601. In someembodiments, operation 605 may be performed by the rib model trackingunit 606. The second portion of the target rib may be determined usingartificial intelligence based model tracking. In some embodiments, thesecond portion of the target rib may be tracked from the starting pointto the second end of the target rib. An exemplary process for ribsegmentation using artificial intelligence based model tracking isillustrated in FIG. 7B. One or more rib models may be used in modeltracking. The rib model(s) may include a 3D geometry. The 3D geometrymay have various shapes including, for example, a cylinder, acylindroid, a cone, or the like, or any combination thereof. The 3Dgeometry may have one or more parameters regarding the size of the 3Dgeometry. For a cylindrical model, the parameters may include a radiusr, a height h₁, etc. For a cylindroid model, the parameters may includea semi-major axis a, a semi-minor axis b, a height h₂, etc. In someembodiments, one or more of the parameters may be adjusted automaticallyor manually during the rib model tracking process.

In 607, a segmented rib may be obtained by segmenting at least oneportion of the target rib. In some embodiments, operation 607 may beperformed by the rib boundary extraction unit 608. In some embodiments,the boundary of the target rib may be extracted based on one or morealgorithms including, for example, a Roberts edge detection algorithm, aSobel edge detection algorithm, a Prewitt edge detection algorithm, aLaplacian edge detection algorithm, a Log edge detection algorithm, aCanny edge detection algorithm, an algorithm based on facet model, orthe like, or any combination thereof. In some embodiments, the firstportion of the target rib and the second portion of the target rib maybe combined to obtain the target rib.

It should be noted that the above description of the process 600 ismerely provided for the purpose of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be made tothe process 600 under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. For example, operation 603 may be omitted. Asanother example, the entire rib (e.g., the first portion and the secondportion of the rib) may be segmented using artificial intelligence basedmodel tracking. As still another example, an operation for determining arib region containing at least a portion of a plurality of ribs may beadded before 601. In some embodiments, the rib region may be determinedby the rib pre-segmentation sub-module 504. As a further example, anoperation for selecting at least one rib of the plurality of ribs as thetarget rib to be segmented may be added before 601. As still a furtherexample, one or more classification-probability-maps may be generated,and the starting point may be determined based on theclassification-probability-map(s). In some embodiments, process 600 forsegmenting a rib may be modified as follows: image data may be acquired,wherein the image data may include a plurality of ribs; a rib regioncontaining at least a portion of the plurality of ribs may bedetermined; at least one rib of the plurality of ribs may be selected asa target rib based on the rib region; at least one rib-probability-maprelating to the target rib may be generated based on an artificialintelligence algorithm; a starting point of the target rib may bedetermined based on the image data, wherein the starting point mayindicate a starting position for tracking the target rib; at least oneportion of the target rib may be tracked based on the starting point andthe at least one rib-probability-map; a segmented rib may be obtained bysegmenting the at least one portion of the target rib. In someembodiments, “rib-probability-map” and “classification-probability-map”may be used interchangeably.

FIG. 7A is a schematic diagram illustrating an exemplary rib modeltracking unit 606 according to some embodiments of the presentdisclosure. The rib model tracking unit 606 may include a modelprediction sub-unit 710, a model matching sub-unit 720, and a modelreconstruction sub-unit 730. At least a portion of the rib modeltracking unit 606 may be implemented on a computing device asillustrated in FIG. 2 or a mobile device as illustrated in FIG. 3 .

The model prediction sub-unit 710 may predict one or more features of arib segment, including, for example, the shape, size, and/or direction,etc. In some embodiments, the model prediction sub-unit 710 may predictthe rib segment based on a matching of a rib model (e.g., a rib model asdescribed in FIG. 6B) with the image data. The model prediction sub-unit710 may predict the rib segment based on a starting point determined bythe starting point determination unit 602, or a pixel/voxel of asegmented rib. The rib segment may include one or more pixels/voxelsrepresenting a portion of the rib. In some embodiments, the rib segmentmay have the same shape and/or size as the rib model described in FIG.6B. The model prediction sub-unit 710 may include aclassification-probability-map determination block 702, a tracedirection determination block 704, and a trace direction range settingblock 706.

The classification-probability-map determination block 702 may generatea classification-probability-map. The classification-probability-map(also referred to as rib-probability-map) may include a plurality ofpixels/voxels. In the present disclosure,“classification-probability-map” and “rib-probability-map” are usedinterchangeably. The classification probability map may have the samesize as an image corresponding to the image data acquired in 401. Apixel/voxel in the classification probability map may correspond to apixel/voxel in the image. A pixel/voxel value of the image may be a graylevel, CT value, etc., of a pixel/voxel in the image. A pixel/voxelvalue of the classification probability map may be a classificationprobability of a pixel/voxel in the classification probability map. Theclassification may refer to the identification of which kind of bone,organ, or tissue a pixel/voxel belongs to. The classificationprobability may refer to a probability that a pixel/voxel belongs to akind of bone, organ, or tissue.

The classification-probability-map determination block 702 may generatethe classification probability map based on an artificial intelligencealgorithm. The classification-probability-map determination block 702may generate the classification probability map based on a trainedclassifier. A classifier may refer to an artificial intelligencealgorithm that implements classification. For example, a classifier mayinclude a classification algorithm to determine whether a pixel/voxelbelongs to a rib. In some embodiments, the classifier may be trainedwith a plurality of samples, including positive samples and/or negativesamples. In some embodiments, image data related to rib(s) may be usedas positive samples, while image data related to other bones (e.g., avertebra, a sternum, a scapula, a clavicle, etc.) and/or organs (e.g., alung, a liver, etc.) may be used as negative samples. It should be notedthat the samples used to train the classifier may unnecessarily be partof the image data acquired in 401. The samples may include image data ofa plurality of subjects other than the subject described in FIG. 4B. Thesamples may be obtained from the storage device 150, the storage 220,the storage 390, and/or an external database (not shown). The trainedclassifier may be generated before rib segmentation. The trainedclassifier may be stored in the storage device 150, the storage 220, thestorage 390, and/or an external database (not shown) for later use.

Using the trained classifier, a rib pixel/voxel in the image data may beassigned a relatively high probability value, while a non-ribpixel/voxel in the image data may be assigned a relatively lowprobability value. In some embodiments, a high probability value may berelative to the low probability value. For example, a probability valuegreater than a threshold (e.g., 50%, 60%, 70%, 80%, 90%, etc.) may beregarded as a high probability value, while a probability value lowerthan the threshold may be regarded as a low probability value. In someembodiments, pixels/voxels with high probability may have a high grayvalue (or a high brightness) than that with low probability, as shown inFIG. 7E. FIG. 7D is an original rib image. FIG. 7E is a classificationprobability map obtained based on the original rib image in FIG. 7D anda trained classifier. As illustrated in FIG. 7E, the pixel/voxel A maybe assigned a high probability value, while the pixel/voxel B may beassigned a low probability value, which means the pixel/voxel A, but notthe pixel/voxel B, may belong to a rib. The classification probabilitymap may enhance the contrast between a rib region and a non-rib region,and thus the accuracy and robustness of the rib model tracking may beimproved.

The trace direction determination block 704 may determine a tracedirection. There are a plurality of directions in a 2D/3D space. Thetrace direction may refer to a direction for model tracking. The tracedirection may be a direction from an already determined rib pixel/voxel(e.g., a starting point, a rib pixel/voxel determined by the partial ribsegmentation unit 604 and/or the rib model tracking unit 606, etc.) toanother rib pixel/voxel to be determined. The trace directiondetermination block 704 may determine the trace direction based on theclassification probability map. In some embodiments, the trace directiondetermination block 704 may designate a direction along which apixel/voxel with a highest probability at a pixel/voxel in theclassification probability map that corresponds to the alreadydetermined rib pixel/voxel as the trace direction.

The trace direction range setting block 706 may set a trace directionrange. The trace direction range may refer to a range of tracedirections in the 2D/3D space along which the model tracking may beperformed. In some embodiments, the model tracking may be performedalong any trace direction in the 2D/3D space. In some embodiments, themodel tracking may be performed within the trace direction range, and nomodel tracking may be performed outside the trace direction range,thereby saving a tracking time. The trace direction range setting block706 may set the trace direction range based on characteristicinformation of the rib. The characteristic information may include ananatomical structure of the rib, a position of the rib relative toanother bone and/or another tissue, a curvature of a pixel/voxel of therib, or the like, or any combination thereof. In some embodiments, thetrace direction range setting block 706 may generate a classificationprobability map within the trace direction range.

The model matching sub-unit 720 may perform model matching based on thepredicted rib segment. The model matching sub-unit 720 may determinewhether the predicted rib segment belongs to the rib. The model matchingsub-unit 720 may compare the predicted rib segment with a rib model.More descriptions of the rib model may be found elsewhere in the presentdisclosure. See, for example, FIG. 6B and the description thereof. Insome embodiments, the model matching sub-unit 720 may perform modelmatching based on a characteristic value of pixels/voxels in thepredicted rib segment. The characteristic value may include an averagevalue of pixels/voxels in the predicted rib segment, a minimum value ofpixels/voxels in the predicted rib segment, an average value ofpixels/voxels at a boundary of the predicted rib segment, a minimumvalue of pixels/voxels at a boundary of the predicted rib segment, orthe like, or any combination thereof. For example, an average value ofpixels/voxels in the predicted rib segment may be determined andcompared with a threshold value determined based on the rib model. Ifthe comparison result satisfies a condition (e.g., the average value isgreater than the threshold value), the model matching sub-unit 720 maydetermine that the model matching is successful, and accordingly thepredicted rib segment may be determined to belong to the rib. Otherwise,the model matching sub-unit 720 may determine that the predicted ribsegment does not belong to the rib.

In some embodiments, the model matching sub-unit 720 may determine whento terminate the model tracking based on a result of the model matching.For example, if the model matching sub-unit 720 determines that thepredicted rib segment does not belong to the rib, the model matchingsub-unit 720 may determine that the model tracking reaches an end of therib. Thus, the model tracking may be terminated.

The model reconstruction sub-unit 730 may perform model reconstructionbased on one or more rib segments successfully matched by the modelmatching sub-unit 720. In some embodiments, the model reconstructionsub-unit 730 may generate a whole rib based on the matched rib segments.In some embodiments, the model reconstruction sub-unit 730 may modify,adjust, and/or update the rib model based on the matched rib segments.For example, the model reconstruction sub-unit 730 may adjust thedimension(s) of the rib model based on an average size of the matchedrib segments.

It should be noted that the above description of the rib model trackingunit 606 is merely provided for the purpose of illustration, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and/ormodifications may be made to the rib model tracking unit 606 under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, the trace direction range setting block 706 may be removed.As another example, the model prediction sub-unit 710 and the modelmatching sub-unit 720 may be integrated into one sub-unit.

FIG. 7B is a flowchart illustrating an exemplary process 700 for ribmodel tracking according to some embodiments of the present disclosure.In some embodiments, the process 700 may be performed by the rib modeltracking unit 606. At least a portion of the process 700 may beimplemented on a computing device as illustrated in FIG. 2 or a mobiledevice as illustrated in FIG. 3 .

In 701, a trace direction range may be determined based on the imagedata. In some embodiments, operation 701 may be performed by the tracedirection range setting block 706. The trace direction range may bedetermined based on characteristic information of the rib. In someembodiments, the characteristic information may be acquired based onprior knowledge. The characteristic information may include ananatomical structure of the rib, a position of the rib relative toanother bone and/or another tissue, a curvature of a pixel/voxel of therib, or the like, or any combination thereof.

In 703, a classification probability map may be generated within thetrace direction range. In some embodiments, operation 703 may beperformed by the classification-probability-map determination block 702.The classification probability map may be generated based on a trainedclassifier. Using the trained classifier, a rib pixel/voxel in the imagedata may be assigned a relatively high probability value, while anon-rib pixel/voxel in the image data may be assigned a relatively lowprobability value. In some embodiments, a high probability value may berelative to the low probability value. For example, a probability valuegreater than a threshold (e.g., 50%, 60%, 70%, 80%, 90%, etc.) may beregarded as a high probability value, while a probability value lowerthan the threshold may be regarded as a low probability value. In someembodiments, pixels/voxels with high probability may have a high grayvalue (or a high brightness) than that with low probability. Theclassification probability map may enhance the contrast between a ribregion and a non-rib region, and thus the accuracy and robustness of therib model tracking may be improved.

In 705, a trace direction may be determined based on the classificationprobability map generated in 703 to obtain a predicted rib segment. Insome embodiments, operation 705 may be performed by the trace directiondetermination block 704. In some embodiments, a direction along which apixel/voxel with a highest probability at a pixel/voxel in theclassification probability map that corresponds to the alreadydetermined rib pixel/voxel may be determined as the trace direction. Arib segment may be predicted based on the trace direction and a ribmodel as described in FIG. 6B.

In 707, the predicted rib segment obtained in 705 may be matched withone or more rib models. In some embodiments, operation 707 may beperformed by the model matching sub-unit 720. A determination as towhether the predicted rib segment belongs to the rib may be made in 707.The predicted rib segment may be compared with one or more rib models.More descriptions of the rib model may be found elsewhere in the presentdisclosure. See, for example, FIG. 6B and the description thereof. Insome embodiments, the model matching may be performed based on acharacteristic value of pixels/voxels in the predicted rib segment. Thecharacteristic value may include an average value of pixels/voxels inthe predicted rib segment, a minimum value of pixels/voxels in thepredicted rib segment, an average value of pixels/voxels at a boundaryof the predicted rib segment, a minimum value of pixels/voxels at aboundary of the predicted rib segment, or the like, or any combinationthereof. For example, an average value of pixels/voxels in the predictedrib segment may be determined and compared with a threshold valuedetermined based on the rib model. In some embodiments, thecharacteristic value may be determined based on one or moreclassification-probability-maps. If the comparison result satisfies acondition (e.g., the average value is greater than the threshold value),the model matching may be determined to be successful, and accordinglythe predicted rib segment may be determined to belong to the rib.Otherwise, the predicted rib segment may be determined to not belong tothe rib. In some embodiments, the rib model may be adjusted in the modelmatching process. For example, the size of the rib model may be adjustedin the model matching process. In some embodiments, the predicted ribsegment may be adjusted in the model matching process. For example, theorientation, position, size, etc. of the predicted rib segment may beadjusted in a certain range, and a new predicted rib segment may begenerated. In some embodiments, the adjusted predicted rib segment mayhave an improved characteristic value, and may be regarded as a matchedrib segment.

In 709, model reconstruction may be performed to obtain a reconstructedmodel. In some embodiments, operation 709 may be performed by the modelreconstruction sub-unit 730. In some embodiments, model reconstructionmay be performed based on one or more matched rib segments determined in707. In some embodiments, the rib model may be modified, adjusted,and/or updated based on the matched rib segments. For example, thedimension(s) of the rib model may be adjusted based on an average sizeof the matched rib segments. In some embodiments, a target rib may begenerated based on the matched rib segments. In some embodiments, basedon a determination that the predicted rib segment obtained in 705 doesnot match with the rib model(s), model reconstruction may be performedin 709 to obtain a reconstructed model, and a plurality of iterationsmay be performed to obtain the target rib. In some embodiments, based ona determination that the predicted rib segment obtained in 705 does notmatch with the rib model(s), the tracking of the target rib may beterminated. In some embodiments, based on a determination that thepredicted rib segment obtained in 705 matches with the rib model(s), thepredicted rib segment may be designated as a matched portion of thetarget rib, and a next portion of the target rib may be tracked based onthe matched portion of the target rib and theclassification-probability-map (also referred to asrib-probability-map).

FIG. 7C illustrates an exemplary trace direction range of rib modeltracking according to some embodiments of the present disclosure. Asshown in FIG. 7C, a rib may have a plurality of segments (e.g., a firstsegment 752, a second segment 753, a third segment 754, etc.). The ribmay be adhesive to a vertebra 755. Taking into consideration of ananatomical structure of the rib and the vertebra 755, the tracedirection range may have a maximum anterior direction D₁, and a maximumposterior direction D₂.

FIG. 7D illustrates an exemplary original rib image according to someembodiments of the present disclosure.

FIG. 7E illustrates an exemplary classification probability mapaccording to some embodiments of the present disclosure. Theclassification probability map was acquired by processing the originalrib image with a trained classifier. The rib pixel/voxel A may have arelatively higher probability, while the vertebra pixel/voxel B may havea relatively lower probability.

FIG. 8 is a flowchart illustrating an exemplary process 800 forextracting a rib according to some embodiments of the presentdisclosure. At least a portion of the process 800 may be implemented ona computing device as illustrated in FIG. 2 or a mobile device asillustrated in FIG. 3 .

In 801, image data may be acquired. The image data may include one ormore ribs. The image data may be acquired as described in 401.

In 803, a seed point of a rib may be determined in the image data. Theseed point of the rib may be determined based on the position of anorgan (e.g., a lung, a liver, etc.). The seed point may be determined asdescribed in 501.

In 805, pre-segmentation may be performed based on the image data andthe seed point determined in 803 to obtain a preliminary rib. The ribmay be pre-segmented using an edge detection algorithm based on aLaplace operator.

In 807, a determination as to whether the rib pre-segmentation isconsidered successful may be made. The determination may be made basedon position information of the rib pre-segmented in 805 and another bone(e.g., a vertebra, etc.). In some embodiments, one or more image layersof the pre-segmented rib in a coronal plane may be superimposed togenerate a grayscale superposition image. A pixel/voxel value of apixel/voxel in the grayscale superposition image may be a sum ofpixel/voxel values of pixels/voxels in a same position in the imagelayers. In some embodiments, a spatial range of a vertebra may bedetermined based on a region of highest gray values in the grayscalesuperposition image. A spatial range of the rib pre-segmented in 805 maybe compared with the spatial range of the vertebra. In some embodiments,in response to the determination that the spatial range of the rib atleast partially overlaps within the spatial range of the vertebra, thepre-segmented rib may be determined to be adhesive to a vertebra. Thus,the pre-segmentation of the rib may be determined to be unsuccessful(e.g., “No” in operation 807), and the process 800 may proceed to 809 toperform a further segmentation for the rib. In some embodiments, inresponse to the determination that no part of the spatial range of therib is within the coordinate range of the vertebra, the pre-segmentationof the rib may be determined to be successful (e.g., “Yes” in operation807), and the process 800 may proceed to operation 827 to output theextracted rib data.

It should be noted that in some embodiments, the preliminary rib may bedesignated as a target rib for further segmentation based on adetermination that the preliminary rib is adhesive to a vertebra. Insome embodiments, the preliminary rib may be designated as a segmentedrib based on a determination that the preliminary rib is not adhesive toa vertebra. Operations 809 through 825 may be a further segmentation fora target rib that is not successfully pre-segmented in 805.

In 809, a characteristic point in an anatomical structure of the rib maybe designated as a starting point. In some embodiments, thecharacteristic point may be a low point. The low point in an anatomicalstructure of the rib may be a point that is determined to be nearest tothe back of a subject. A threshold algorithm may be employed to extractone or more bones (e.g., ribs, vertebrae, sternum, etc.) in the imagedata. Pixels/voxels may be superimposed in a coronal plane, and aposition with a minimum number of pixels/voxels may be determined as thelow point in the anatomical structure of the rib. In some embodiments,the low point may be determined based on a curvature value of the ribpixels/voxels. For example, a point with a maximum curvature of the ribmay be determined as the low point. In some embodiments, a ribpixel/voxel at the lowest position in the anatomical structure of therib (i.e., a rib pixel/voxel closest to the back of the subject) may bedesignated as a starting point of the rib. For example, ribpixels/voxels (e.g., rib pixels/voxels of one or more image layers in acoronal plane) may be added up (or superimposed) along ananterior-posterior direction (see FIG. 7C) of the subject (e.g., adirection from the front to the back of the subject) to obtain a diagramrepresenting the total number of rib pixels/voxels in each coronalplane. In some embodiments, each element at a position of the diagrammay represent a total number of pixels/voxels. The pixels/voxels may belocated at a corresponding position in one or more of the image layersand may belong to a portion of the rib pixels/voxels, wherein eachpixel/voxel of the portion of the rib pixels/voxels may have a grayvalue larger than a threshold. Then the rib pixels/voxels (or elements)in the diagram may be added up (or superimposed) along asuperior-inferior direction of the subject (e.g., a direction from thehead to the feet of the subject) to obtain a histogram representing adistribution of rib pixels/voxels along a left-right direction of thesubject (e.g., a direction from the left hand to the right hand of thesubject). In some embodiments, each element of the histogram mayrepresent a sum of elements belonging to a portion of all the elementsof the diagram, wherein the portion of all the elements may have a sameposition in a left-right direction in the diagram. In some embodiments,the characteristic point may be determined based on a position in thehistogram. A position in the histogram with a minimum value may bedetermined as a coordinate on an X axis of the lowest position, whereinthe X axis may correspond to a sagittal axis of the subject. A sagittalY-Z plane relating to the coordinate on the X axis may be determined,wherein the Y-Z plane may correspond to a coronal plane of the subject.The starting point may be determined based on an intersection part ofthe Y-Z plane and a pre-segmentation result (e.g., a pre-segmented rib).

In 811, a first portion of the rib may be determined based on thestarting point determined in 809. In an anatomical structure, a rib mayhave two ends, a first end and a second end. In some embodiments, thefirst end may be far from the vertebra, while the second end may beconnected or close to a vertebra. In some embodiments, the first end maybe spaced from the vertebra by a first distance, while the second endmay be spaced from the vertebra by a second distance, and the firstdistance is larger than the second distance. A cross plane passingthrough the starting point may divide the rib into two portions. In someembodiments, the cross plane may refer to a plane perpendicular to atangential direction of an outer surface of the rib at the startingpoint. In some embodiments, the starting point may be closer to thesecond end than to the first end of the rib. The first portion of therib may refer to a rib segment between the starting point to the firstend, while the second portion of the rib may refer to a rib segmentbetween the starting point to the second end. A region growing algorithmmay be employed to segment the first portion of the rib. Using regiongrowing segmentation, pixel(s)/voxel(s) that are adjacent to thestarting point and satisfy one or more conditions may be iterativelyextracted as rib pixels/voxels. One or more limitations may be set forsegmenting the first portion of the rib when the region growingalgorithm is involved. In some embodiments, the region growing may beperformed along a direction from the starting point to the second end ofthe rib, and a newly grown region corresponding to the rib may belimited not to go beyond the cross plane passing through the startingpoint that separates the first portion and the second portion of therib. In some embodiments, image data corresponding to the rib segmentedusing region growing may need to satisfy a condition. For example, suchimage data may satisfy a certain derivative (e.g., a second derivative)relationship, fall within a threshold range, or the like, or anycombination thereof.

Operations 813 through 819 may be performed to determine a secondportion of the rib.

In 813, a preliminary rib segment of a second portion of the rib may bedetermined in the image data based on the starting point determined in809 and/or a model tracking algorithm. The image data may be processedwith a trained classifier to generate a classification probability map.A plurality of directions at the starting point may be searched and thedirection along which a pixel/voxel with a highest probability at apixel/voxel in the classification probability map that corresponds tothe already determined rib pixel/voxel may be designated as apreliminary trace direction. The preliminary rib segment of the secondportion of the rib may be determined using model tracking along thepreliminary trace direction. The preliminary rib segment may be matchedwith a rib model, as described in 707. More descriptions of the ribmodel may be found elsewhere in the present disclosure. See, forexample, FIG. 6B and the description thereof. If the preliminary ribsegment is considered matched successfully with the rib model, thepreliminary rib segment may be designated as a real rib segment (or amatched rib segment).

In 815, a classification probability map within a preset trace directionrange may be generated using a classifier. In some embodiments, theclassification probability map may be generated as described in 703. Insome embodiments, a small trace direction range may be set towards theposterior direction, while a large trace direction range may be settowards the anterior direction, since the rib may curve towards theanterior direction to approach a vertebra (as shown in FIG. 7C). In someembodiments, the trace direction range may be illustrated as an anglerange. The angle may refer to an intersection angle of a trace directionand a linear extension direction of the preliminary rib. For example, arange of 45 degrees towards the anterior direction and a range of 15degrees towards the posterior directions may be designated as the tracedirection range (e.g., the range between D₁ and a in FIG. 7C).

In 817, a trace direction may be determined based on the classificationprobability map to obtain a predicted rib segment. A direction alongwhich a pixel/voxel with a highest probability at a pixel/voxel in theclassification probability map generated in 815 that corresponds to thealready determined rib pixel/voxel may be designated as the tracedirection, and thus a predicted rib segment may be obtained.

In 819, the predicted rib segment obtained in 817 may be matched withone or more rib models. The predicted rib segment may be compared withthe rib model(s), as described in 707. More descriptions of the ribmodel may be found elsewhere in the present disclosure. See, forexample, FIG. 6B and the description thereof. The predicted rib segmentmay have a similar shape and/or size with the rib model. For example, ifa cylinder is used as a rib model in model tracking, the predicted ribsegment obtained in 817 may have a cylindrical shape. In someembodiments, an average value of pixels/voxels of the rib segment may bedetermined and compared with a threshold relating to the rib model.

In 821, a determination as to whether the predicted rib segment matcheswith the model(s) may be made. In some embodiments, if a differencebetween the predicted rib segment and a preset model (i.e., the ribmodel) is within a threshold, then the predicted rib segment may bedetermined to match with the preset model. For example, it may bedetermined that the predicted rib segment matches with the preset modelwhen a difference between the average value of pixels/voxels in thepredicted rib segment and that in the preset model is less than thethreshold. As another example, it may be determined that the predictedrib segment does not match with the preset model when the differencebetween the average value of pixels/voxels in the predicted rib segmentand that in the preset model is no less than the threshold. The process800 may return to 815 in response to the determination that thepredicted rib segment matches with the preset model (e.g., “Yes” inoperation 821). In some embodiments based on a determination that thepredicted rib segment matches with the rib model(s), the predicted ribsegment may be designated as a matched rib segment of a target rib, anda next rib segment of the target rib may be tracked based on the matchedrib segment of the target rib and/or a reconstructed model. In someembodiments, a plurality of iterations may be performed based on thereconstructed model and/or the matched rib segment of the target rib toobtain a next rib segment of the target rib. The process 800 may proceedto 823 in response to the determination that the predicted rib segmentdoes not match with the preset model.

In 823, model reconstruction may be performed based on one or morematched rib segments determined in 813 through 821 and/or the firstportion of the rib determined in 811 to form a rib. In some embodiments,based on a determination that the predicted rib segment does not matchwith the rib model(s), the tracking of at least one portion of a targetrib may be terminated. In some embodiments, based on a determinationthat the predicted rib segment does not match with the rib model(s),model reconstruction may be performed to obtain a reconstructed modelbased on one or more matched rib segments. In some embodiments, at leastone portion of a target rib may be extracted based on the matched ribsegment(s) and/or the reconstructed model.

In 825, a boundary of the rib may be extracted. The boundary of the ribmay be extracted based on one or more algorithms mentioned in thepresent disclosure. See, for example, FIG. 6B and the descriptionthereof.

In 827, the extracted rib data may be outputted. In some embodiments,the extracted rib data may be outputted through the I/O 230 for display.In some embodiments, the extracted rib data may be outputted to aterminal 130. In some embodiments, the extracted rib data may beoutputted to a storage (e.g., the storage device 150, the storage 220,the storage 390, etc.) for storing.

It should be noted that the above description about the process 800 forextracting a rib is merely provided for the purpose of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and/ormodifications may be made under the teachings of the present disclosure.However, those variations and modifications do not depart from the scopeof the present disclosure. For example, operation 811 may be performedafter 813 through 821. It may be indicated that the first portion of therib may be determined after the second portion of the rib is determined.As another example, operations 813 through 821 may be performedsimultaneously with 811. One or more other operations may be added toprocess 800, or one or more operations may be omitted from process 800.For example, operations 805 through 807 may be omitted. It may beindicated that there may be no pre-segmentation of ribs.

FIG. 9 is a flowchart illustrating an exemplary process 900 forlabelling one or more ribs according to some embodiments of the presentdisclosure. At least a portion of the process 900 may be implemented ona computing device as illustrated in FIG. 2 or a mobile device asillustrated in FIG. 3 .

In 901, image data may be acquired. The image data may be acquired asdescribed in 401. It should be noted that the image data may include aplurality of lung pixels/voxels that correspond to a left lung and/or aright lung. In some embodiments, one or more lung voxels may be referredto as lung volume data.

In 903, one or more connected domains (or regions) of the ribs may bedetermined by performing rib segmentation based on the image data. Oneor more ribs may be segmented based on one or more segmentationalgorithms mentioned in the present disclosure. The ribs may besegmented as described in the process 500, and/or the process 600. Insome embodiments, a rib may have one or more connected domains (orregions). A connected domain (or region) may include one or moreneighboring rib pixels/voxels.

In 905, a middle image layer of the image data in a coronal plane nearthe middle of an object (e.g., a lung of the object) may be obtained. Insome embodiments, an image layer among all image layers in a coronalplane with a greatest number of ribs may be regarded as the middle imagelayer. In some embodiments, an image layer among all image layers in acoronal plane with a largest projected area of a left lung and/or aright lung may be regarded as the middle image layer. In someembodiments, an image layer located in the middle layer in a coronalplane may be regarded as the middle image layer. In some embodiments,one or more lung masks in the coronal plane may be determined, and alung mask of a middle layer in the anterior-posterior direction may beregarded as the middle image layer. In some embodiments, the middleimage layer may refer to a layer that is located at a midpoint in theanterior-posterior direction. For example, for CT image data with aresolution of 512×512 in a transverse plane, the 256th image layer in acoronal plane may be selected as the middle image layer. In someembodiments, an image layer located near the middle image layer may alsobe regarded as a middle image layer. In some embodiments, the middleimage layer may be adjusted based on an anatomical structure of the ribsand other bones (e.g., a vertebra). For example, if the middle imagelayer contains any part of a vertebra, another middle image layer in thecoronal plane that does not contain any part of a vertebra may beselected among the image layers towards the anterior direction or theposterior direction. For instance, for CT image data with a resolutionof 512×512 in a transverse plane, the obtained middle image layer may beone of the 240th to 260th image layers in the coronal plane. As anotherexample, the middle image layer in the coronal plane may contain one tonine (or ten) pairs of ribs, while the residual ribs (e.g., floatingribs) may not be shown in the middle image layer. In some embodiments, afloating rib may refer to one of the 11th and 12th pairs of ribs.

In 907, a lung mask in the middle image layer may be obtained. Ribs mayhave a barrel shape surrounding a left lung and/or a right lung in termsof the anatomical structure. In some embodiments, the lung mask may beobtained based on the middle image layer and one or more segmentationalgorithms mentioned in the present disclosure. FIG. 10A illustrates anexemplary middle image layer in the coronal plane obtained in 905. Alung mask 1002-1 for the left lung and a lung mask 1002-2 for the rightlung are illustrated in FIG. 10A.

In 909, the lung mask may be dilated. In some embodiments, an originallayer corresponding to the middle image layer may be determined, theoriginal layer may be binarized and dilated, and then the dilated lungmask may be recognized. The lung mask may include a region correspondingto one or more ribs. A dilating operation may refer to the expansion ofa shape of the lung mask based on a structural element. Ribs may beincluded in the dilated lung mask. One or more parameters may be used inthe dilating operation, for example, a lung mask region in the imagedata to be dilated, a size of the structural element, or the like, orany combination thereof. The parameters may be predetermined based onone or more empirical values. In some embodiments, the extent of thedilation may be determined based on characteristics of the image dataincluding, for example, an image resolution, a characteristic of thesubject (e.g., a size of a lung of the subject, etc.), or the like, orany combination thereof. FIG. 10B illustrates an exemplary middle imagelayer with a dilated lung mask in the coronal plane. A dilated lung mask1004-1 for the left lung and a dilated lung mask 1004-2 for the rightlung are illustrated in FIG. 10B.

In 911, one or more seed points of one or more ribs may be identifiedbased on the dilated lung mask and/or a threshold relating to a graylevel. The seed point(s) may be determined by performing a thresholdsegmentation based on the dilated lung mask. For example, the gray levelof a bone may be relatively high (e.g., higher than 120 HU), and thegray level of a lung may be relatively low (e.g., lower than −800 HU),and thus, a gray value higher than a certain level may be determined asthe threshold. In some embodiments, seed points of the 1st to 9th (or10th) pair of ribs may be identified based on the dilated lung mask inthe coronal plane, while the seed points of the 10th (or 11th) to 12thpairs of ribs (also referred to as “residual ribs” or “floating ribs”)may be determined in a transverse plane as illustrated below. FIG. 10Cillustrates an exemplary middle image layer with ten pairs of ribs inthe coronal plane according to some embodiments of the presentdisclosure. In some embodiments, the seed points determined according tothe operations 905 through 911 may not be affected by a vertebra, andthe accuracy for the rib labelling may be improved.

In 913, one or more image layers in a transverse plane of the image datacontaining residual rib(s) not included in the middle image layer may bedetermined. In some embodiments, the position of a pair of ribs farthestaway from the head (also referred to as “the lowest pair of ribs”, or apair of ribs that have larger coordinate values than other ribs in thesuperior-inferior direction) in the middle image layer in the coronalplane may be determined. See, for example, the line 1005 in FIG. 10C.The residual ribs (e.g., the floating ribs) may be found in one or moreimage layers in the transverse plane based on the position of the lowestpair of ribs (or the pair of ribs that have larger coordinate valuesthan other ribs in the superior-inferior direction) in the coronalplane. As illustrated in FIG. 10C, the residual ribs may be found in theimage layers in the transverse plane below the line 1005. FIG. 10Dillustrates an exemplary image layer (e.g., below the line 1005) in thetransverse plane with a pair of residual ribs according to someembodiments of the present disclosure.

In 915, one or more seed points of the residual rib(s) may be identifiedin the transverse plane. In some embodiments, the seed point(s) of theresidual rib(s) may be identified based on a threshold segmentationalgorithm.

In 917, a first seed point of a first rib may be labelled based on aposition of a reference pixel or voxel relating to a reference organ. Insome embodiments, the reference organ may be a lung or a liver. In someembodiments, the reference pixel or voxel may relate to the apex of alung or the base of a liver. In some embodiments, the seed pointsclosest to the apex of a lung may be labelled as a first rib. Forexample, a seed point whose distance to the apex of the left lung iswithin a threshold may be labelled as “first left rib.” As anotherexample, a seed point whose distance to the apex of the right lung iswithin a threshold may be labelled as “first right rib.” In someembodiments, the “twelfth left rib” and/or the “twelfth right rib” maybe determined based on the position of the liver and/or the stomach. Forexample, a liver image layer including the lower border of the liver inthe transverse plane may be determined, rib pixels/voxels may be foundin the Z axis direction (i.e., from the head to the feet of a subject)in one or more image layers starting from the liver image layer, thelast found rib pixel/voxel below the left lung or the stomach may bedesignated as the “twelfth left rib,” and the last found rib pixel/voxelbelow the liver may be designated as the “twelfth right rib.” In someembodiments, the position of the apex of the lung may be determinedbased on a curvature value of pixels/voxels of the lung. For example, apixel/voxel with a maximum curvature value of the lung may be determinedas the apex of the lung. In some embodiments, the position of the apexof the lung may be determined using an artificial intelligencealgorithm.

In 919, a second seed point of a second rib may be labelled based on arelative position between the first seed point and the second seedpoint. In some embodiments, if the “first left rib” or the “first rightrib” is labelled in 917, seed points of other ribs may be successivelylabelled as “second left rib,” “second right rib,” “third left rib,”“third right rib,” etc. in order from top to bottom. In someembodiments, if the “twelfth left rib” or the “twelfth right rib” islabelled in 917, seed points of other ribs may be successively labelledas “eleventh left rib,” “eleventh right rib,” “tenth left rib,” “tenthright rib,” etc. in order from bottom to top. In some embodiments, animage layer in the coronal plane may include twelve pairs of ribs, andthe seed points corresponding to the twelve pairs of ribs may belabelled based on the position of the apex of a lung or the base of aliver. In some embodiments, the image data may include only a portion ofa thorax (e.g., the image data may include a first pair of ribs but nota twelfth pair of ribs), the seed points may be successively labelledbased on the apex of a lung. In some embodiments, the image data mayinclude only a portion of a thorax (e.g., the image data may include atwelfth pair of ribs but not a first pair of ribs), the seed points maybe successively labelled based on the base of a liver.

It should be noted that 917 through 919 may be executed simultaneouslywith 911 through 915. It may be indicated that a seed point may belabelled once it is identified. In some embodiments, the seed points maybe labelled after all the seed points are identified. Alternatively,seed points of a twelfth pair of ribs may be firstly labelled as“twelfth left rib” and “twelfth right rib,” and seed points of otherribs may be successively labelled as “eleventh left rib,” “eleventhright rib,” “tenth left rib,” “tenth right rib,” etc. in order frombottom to top.

In 921, the connected domains (or regions) of ribs may be labelled basedon position information of the labelled seed point(s) of the ribs andthe connected domains of ribs. In some embodiments, a hit-or-missoperation may be performed. A hit-or-miss operation may refer to amatching operation for matching the labelled seed point(s) of ribs withthe connected domains of ribs. In some embodiments, it may be determinedto be “hit” if a labelled seed point and a part of a connected domain ofa rib have the same position information. In some embodiments, it may bedetermined to be “miss” if the labelled seed point and no part of theconnected domain of the rib have the same position information. Forexample, when a seed point labelled as “first left rib” has a positionwithin a spatial range of the connected domain of a rib, the rib may belabelled as “first left rib.” All the ribs determined in 903 may belabelled likewise, as illustrated in FIG. 10E. FIG. 10E illustratesexemplary labelled ribs according to some embodiments of the presentdisclosure.

It should be noted that the above description about the process 900 forlabelling one or more ribs is merely provided for the purpose ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and/or modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. For example, operation903 may be performed after operation 919. It may be indicated that therib segmentation may be performed independently with the identificationand labelling of the seed point(s), thus the rib segmentation may beperformed after seed points of ribs are identified and labelled. In someembodiments, process 900 may be modified as follows: a medical image maybe acquired, wherein the medical image may include a plurality of voxelscorresponding to a plurality of ribs; a plurality of seed points of aplurality of first connected domains (or regions) of the plurality ofribs may be identified based on a recognition algorithm; the medicalimage may be segmented to obtain a plurality of second connected domains(or regions) of the plurality of ribs; the plurality of ribs may belabelled by matching the first connected domains (or regions) includingthe plurality of seed points with the second domains (or regions) of theplurality of ribs.

FIG. 11A through 11D illustrate exemplary test images of ribsegmentation using artificial intelligence based model trackingaccording to some embodiments of the present disclosure. The images inthe top left corner of FIG. 11A through 11D illustrate different ribimages in the transverse plane. The images in the top right corner ofFIG. 11A through 11D illustrate different rib images in the sagittalplane. The images in the lower left corner of FIG. 11A through 11Dillustrate different segmented ribs. The images in the lower rightcorner of FIG. 11A through 11D illustrate different rib images in thecoronal plane.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, for example, aninstallation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. An image processing method implemented on atleast one machine each of which has at least one processor and onestorage, the method comprising: acquiring image data, the image dataincluding a plurality of ribs; determining a rib region containing atleast a portion of the plurality of ribs; selecting, based on the ribregion, at least one rib of the plurality of ribs as a target rib;generating, based on an artificial intelligence algorithm, at least onerib-probability-map relating to the target rib; determining, based onthe image data, a starting point of the target rib, the starting pointindicating a starting position for tracking the target rib; tracking,based on the starting point and the at least one rib-probability-map, atleast one portion of the target rib, wherein the at least one portion ofthe target rib is determined based on at least one rib model including:determining, based on the at least one rib-probability-map, a predictedrib segment; matching the predicted rib segment with the at least onerib model; and in response to a determination that the predicted ribsegment does not match with the at least one rib model, terminatingtracking the at least one portion of the target rib; or in response to adetermination that the predicted rib segment matches with the at leastone rib model, designating the predicted rib segment as a matched ribsegment of the target rib; and obtaining a segmented rib by segmentingthe at least one portion of the target rib.
 2. The method of claim 1,wherein the selecting, based on the rib region; at least one rib of theplurality of ribs as a target rib comprises: determining a seed pointfor the at least one rib of the plurality of ribs; performingpre-segmentation based on the image data and the seed point to obtain apreliminary rib; and designating, based on a determination that thepreliminary rib is adhesive to a vertebra, the preliminary rib as thetarget rib for further segmentation, or designating, based on adetermination that the preliminary rib is not adhesive to a vertebra,the preliminary rib as the segmented rib.
 3. The method of claim 1;wherein the determining a starting point of the target rib comprises:determining a histogram based on a plurality of image layers of thetarget rib in a coronal plane; and designating, based on the histogram,a characteristic point of the target rib as the starting point.
 4. Themethod of claim 3, wherein the determining a histogram comprises:superimposing a plurality of rib pixels or voxels of the plurality ofimage layers along an anterior-posterior direction to obtain a diagram,each element at a position of the diagram representing a total number ofpixels or voxels that are located at a corresponding position in one ormore of the plurality of image layers and belong to a portion of theplurality of rib pixels or voxels, wherein each pixel or voxel of theportion of the plurality of rib pixels or voxels has a gray value largerthan a first threshold; and superimposing a plurality of elements of thediagram along a superior-inferior direction to obtain the histogram,each element of the histogram representing a sum of elements belongingto a portion of the plurality of elements, wherein all of the portion ofthe plurality of elements have a same position in a left-rightdirection.
 5. The method of claim 3, wherein the characteristic point isdetermined based on a position in the histogram, wherein a point at theposition has a minimum value in the histogram.
 6. The method of claim 1,wherein the generating at least one rib-probability-map relating to thetarget rib comprises: generating, based on a classifier, the at leastone rib-probability-map, wherein the classifier is trained based on theartificial intelligence algorithm and a plurality of images relating toat least one sample rib.
 7. The method of claim 1, wherein thedetermining, based on the at least one rib-probability-map, a predictedrib segment comprises: determining, based on the image data, a tracedirection range; and determining, based on the trace direction range andthe at least one rib-probability-map, the predicted rib segment.
 8. Themethod of claim 7, wherein the determining the predicted rib segmentcomprises: determining, within the trace direction range, at least oneportion of the at least one rib-probability-map; determining, based onthe at least one portion of the at least one rib-probability-map, atrace direction; and predicting, based on the trace direction, thepredicted rib segment.
 9. The method of claim 1, further comprising: inresponse to a determination that the predicted rib segment does notmatch with the at least one rib model, performing, based on a pluralityof matched rib segments, model reconstruction to obtain a reconstructedmodel; and extracting, based on the plurality of matched rib segments,the at least one portion of the target rib.
 10. The method of claim 1,further comprising: tracking, based on the matched rib segment of thetarget rib and the at least one rib-probability-map, a next rib segmentof the target rib.
 11. The method of claim 1, wherein the target rib hasa first end and a second end, wherein the first end of the target rib isspaced from a vertebra by a first distance, and the second end of thetarget rib is spaced from the vertebra by a second distance, and thefirst distance is larger than the second distance.
 12. The method ofclaim 11, wherein the determining a starting point of the target ribcomprises: designating a point of the target rib closer to the secondend than to the first end of the target rib as the starting point. 13.The method of claim 11, wherein the tracking at least one portion of thetarget rib comprises: tracking the at least one portion of the targetrib from the starting point to the second end of the target rib.
 14. Themethod of claim 11, wherein the obtaining a segmented rib by segmentingthe at least one portion of the target rib comprises: segmenting a firstportion of the target rib using a first segmentation algorithm, whereinthe first portion includes a region between the starting point and thefirst end of the target rib; and combining the first portion of thetarget rib and the segmented rib to obtain the target rib.
 15. Themethod of claim 14, wherein the first segmentation algorithm is a regiongrowing algorithm.
 16. The method of claim 1, further comprising:labelling the segmented rib.
 17. A system comprising: at least oneprocessor, and a storage configured to store instructions, theinstructions, when executed by the at least one processor, causing thesystem to effectuate a method comprising: acquiring image data, theimage data including a plurality of ribs; determining a rib regioncontaining at least a portion of the plurality of ribs; selecting, basedon the rib region, at least one rib of the plurality of ribs as a targetrib; generating; based on an artificial intelligence algorithm, at leastone rib-probability-map relating to the target rib; determining, basedon the image data, a starting point of the target rib, the startingpoint indicating a starting position for tracking the target rib;tracking, based on the starting point and the at least onerib-probability-map, at least one portion of the target rib, wherein theat least one portion of the target rib is determined based on at leastone rib model including: determining, based on the at least onerib-probability-map, a predicted rib segment; matching the predicted ribsegment with the at least one rib model; and in response to adetermination that the predicted rib segment does not match with the atleast one rib model, terminating tracking the at least one portion ofthe target rib; or in response to a determination that the predicted ribsegment matches with the at least one rib model, designating thepredicted rib segment as a matched rib segment of the target rib; andobtaining a segmented rib by segmenting the at least one portion of thetarget rib.
 18. The system of claim 17, wherein the selecting, based onthe rib region, at least one rib of the plurality of ribs as a targetrib comprises: determining a seed point for the at least one rib of theplurality of ribs; performing pre-segmentation based on the image dataand the seed point to obtain a preliminary rib; and designating, basedon a determination that the preliminary rib is adhesive to a vertebra,the preliminary rib as the target rib for further segmentation, ordesignating, based on a determination that the preliminary rib is notadhesive to a vertebra, the preliminary rib as the segmented rib. 19.The system of claim 17, wherein the determining a starting point of thetarget rib comprises: determining a histogram based on a plurality ofimage layers of the target rib in a coronal plane; and designating,based on the histogram, a characteristic point of the target rib as thestarting point.
 20. A non-transitory computer readable medium storinginstructions, the instructions, when executed by at least one processor,causing the at least one processor to implement a method comprising:acquiring image data, the image data including a plurality of ribs;determining a rib region containing at least a portion of the pluralityof ribs; selecting, based on the rib region, at least one rib of theplurality of ribs as a target rib; generating, based on an artificialintelligence algorithm, at least one rib-probability-map relating to thetarget rib; determining, based on the image data, a starting point ofthe target rib, the starting point indicating a starting position fortracking the target rib; tracking, based on the starting point and theat least one rib-probability-map, at least one portion of the targetrib, wherein the at least one portion of the target rib is determinedbased on at least one rib model including: determining, based on the atleast one rib-probability-map, a predicted rib segment; matching thepredicted rib segment with the at least one rib model; and in responseto a determination that the predicted rib segment does not match withthe at least one rib model, terminating tracking the at least oneportion of the target rib; or in response to a determination that thepredicted rib segment matches with the at least one rib model,designating the predicted rib segment as a matched rib segment of thetarget rib; and obtaining a segmented rib by segmenting the at least oneportion of the target rib.